Computer vision Books

Book SynopsisGet started in the rapidly expanding field of computer vision with this practical guide. Written by Adrian Kaehler and Gary Bradski, creator of the open source OpenCV library, this book provides a thorough introduction for developers, academics, roboticists, and hobbyists.

Read more less

Book SynopsisGenerative AI is the hottest topic in tech. This practical book teaches machine learning engineers and data scientists how to use TensorFlow and Keras to create impressive generative deep learning models from scratch

Read more less

Book SynopsisFor Readers of Ray Kurzweil and Michio Kaku, a New Look at the Cutting Edge of Artificial Intelligence Imagine a robotic stuffed animal that can read and respond to a child’s emotional state, a commercial that can recognize and change based on a customer’s facial expression, or a company that can actually create feelings as though a person were experiencing them naturally. Heart of the Machine explores the next giant step in the relationship between humans and technology: the ability of computers to recognize, respond to, and even replicate emotions. Computers have long been integral to our lives, and their advances continue at an exponential rate. Many believe that artificial intelligence equal or superior to human intelligence will happen in the not-too-distance future; some even think machine consciousness will follow. Futurist Richard Yonck argues that emotion, the first, most basic, and most natural form of communication, is at the heart of how we will soon work with and use computers. Instilling emotions into computers is the next leap in our centuries-old obsession with creating machines that replicate humans. But for every benefit this progress may bring to our lives, there is a possible pitfall. Emotion recognition could lead to advanced surveillance, and the same technology that can manipulate our feelings could become a method of mass control. And, as shown in movies like Her and Ex Machina, our society already holds a deep-seated anxiety about what might happen if machines could actually feel and break free from our control. Heart of the Machine is an exploration of the new and inevitable ways in which mankind and technology will interact. The paperback edition has a new foreword by Rana el Kaliouby, PhD, a pioneer in artificial emotional intelligence, as well as the cofounder and CEO of Affectiva, the acclaimed AI startup spun off from the MIT Media Lab.Trade Review"Yonck is a sure-footed guide and is not without a sense of humor . . . [He] provides a compelling and thorough history of the interaction between our emotional lives and our technology." —Ray Kurzweil, The New York Times Book Review "A fascinating, and sometimes disturbing, look at a rapidly approaching future where smart machines understand and manipulate our emotions—and ultimately bond with us in ways that blur the line between ourselves and our technology." —Martin Ford, New York Times bestselling author of Rise of the Robots: Technology and the Threat of a Jobless Future “Richard Yonck’s Heart of the Machine is a fascinating speculation on the near- and far-term significance of emotions for user interfaces, machine-mediated communication between humans, and what technology and humans may become.” —Vernor Vinge, computer scientist and Hugo Award–winning author of Rainbows End “Your world is about to change in shocking and amazing ways. The line between machines and humanity is blurring giving us a strange and beautiful tomorrow. Yonck takes us on a journey through this world from the science and technology of today and into the possibilities and perils that lay just over the horizon. If you want to catch a glimpse of the future open this book.” —Brian David Johnson, former chief futurist at Intel and founder of the 21st Century Robot Project "[Yonck] makes a compelling argument for why affective computing (technology that can read, interpret, replicate, and experience emotions and use those abilities to influence us) is the key to AI and the heart of how we will work with computers. . . . an engaging read." —Library Journal “Very important for any decision-maker and a must-read for corporations for planning their road map. It is also recommended to everyone who is curious enough to understand the future. Even the very near future.” —Yoram Levanon, chief science officer at Beyond Verbal Communication, Ltd. "How we interact with technology is changing: it is becoming more relational and conversational. Yonck makes a very strong case why our devices and advanced AI systems need to have emotional intelligence, specifically the ability to sense human emotions and adapt accordingly. This book highlights key considerations both for academic researchers as well as business leaders looking for commercial applications of AI." —Rana el Kaliouby, cofounder and CEO of Affectiva "By using the futurist’s most valuable communications tool—the scenario—to introduce his chapters, Yonck moves between anecdotes from research in affective computing and AI/robotics to speculative scenarios, all with the even hand of a skilled storyteller.” —Cynthia G. Wagner, consulting editor at Foresight Signals, former editor of The Futurist magazine

Read more less

Book Synopsis

Read more less

Book Synopsis

Read more less

Book SynopsisUnderstanding Geometric Algebra: Hamilton, Grassmann, and Clifford for Computer Vision and Graphics introduces geometric algebra with an emphasis on the background mathematics of Hamilton, Grassmann, and Clifford. It shows how to describe and compute geometry for 3D modeling applications in computer graphics and computer vision.Unlike similar texts, this book first gives separate descriptions of the various algebras and then explains how they are combined to define the field of geometric algebra. It starts with 3D Euclidean geometry along with discussions as to how the descriptions of geometry could be altered if using a non-orthogonal (oblique) coordinate system. The text focuses on Hamiltonâs quaternion algebra, Grassmannâs outer product algebra, and Clifford algebra that underlies the mathematical structure of geometric algebra. It also presents points and lines in 3D as objects in 4D in the projective geometry framework; explores conformal geometryTrade Review"Several software tools are available for executing geometric algebra, but the purpose of the book is to bring about a deeper insight and interest in the theory on which these tools are based."—Zentralblatt MATH 1319Table of ContentsIntroduction. 3D Euclidean Geometry. Oblique Coordinate Systems. Hamilton's Quaternion Algebra. Grassmann's Outer Product Algebra. Geometric Product and Clifford Algebra. Homogeneous Space and Grassmann-Cayley Algebra. Conformal Space and Conformal Geometry: Geometric Algebra. Camera Imaging and Conformal Transformations. Answers. Bibliography. Index.

Read more less

Book SynopsisIntroduction.- Fingerprint Recognition.- Face Recognition.- Iris Recognition.- Additional Biometric Traits.- Multibiometrics.- Security of Biometric Systems.Table of ContentsIntroduction.- Fingerprint Recognition.- Face Recognition.- Iris Recognition.- Additional Biometric Traits.- Multibiometrics.- Security of Biometric Systems.

Read more less

Book SynopsisComputer vision encompasses the construction of integrated vision systems and the application of vision to problems of real-world importance. The process of creating 3D models is still rather difficult, requiring mechanical measurement of the camera positions or manual alignment of partial 3D views of a scene.Trade Review“This text is a valuable reference for practitioners and programmers working in 3D computer vision, image processing and analysis as well as computer visualisation. It would also be of interest to advanced students and researchers in the fields of engineering, computer science, clinical photography, robotics, graphics and mathematics.” (Zentralblatt MATH, 2012) Table of ContentsPreface xv Acknowledgements xvii Notation and Abbreviations xix Part I 1 1 Introduction 3 1.1 Stereo-pair Images and Depth Perception 4 1.2 3D Vision Systems 4 1.3 3D Vision Applications 5 1.4 Contents Overview: The 3D Vision Task in Stages 6 2 Brief History of Research on Vision 9 2.1 Abstract 9 2.2 Retrospective of Vision Research 9 2.3 Closure 14 2.3.1 Further Reading 14 Part II 15 3 2D and 3D Vision Formation 17 3.1 Abstract 17 3.2 Human Visual System 18 3.3 Geometry and Acquisition of a Single Image 23 3.3.1 Projective Transformation 24 3.3.2 Simple Camera System: the Pin-hole Model 24 3.3.3 Projective Transformation of the Pin-hole Camera 28 3.3.4 Special Camera Setups 29 3.3.5 Parameters of Real Camera Systems 30 3.4 Stereoscopic Acquisition Systems 31 3.4.1 Epipolar Geometry 31 3.4.2 Canonical Stereoscopic System 36 3.4.3 Disparity in the General Case 38 3.4.4 Bifocal, Trifocal and Multifocal Tensors 39 3.4.5 Finding the Essential and Fundamental Matrices 41 3.4.6 Dealing with Outliers 49 3.4.7 Catadioptric Stereo Systems 54 3.4.8 Image Rectification 55 3.4.9 Depth Resolution in Stereo Setups 59 3.4.10 Stereo Images and Reference Data 61 3.5 Stereo Matching Constraints 66 3.6 Calibration of Cameras 70 3.6.1 Standard Calibration Methods 71 3.6.2 Photometric Calibration 73 3.6.3 Self-calibration 73 3.6.4 Calibration of the Stereo Setup 74 3.7 Practical Examples 75 3.7.1 Image Representation and Basic Structures 75 3.8 Appendix: Derivation of the Pin-hole Camera Transformation 91 3.9 Closure 93 3.9.1 Further Reading 93 3.9.2 Problems and Exercises 94 4 Low-level Image Processing for Image Matching 95 4.1 Abstract 95 4.2 Basic Concepts 95 4.2.1 Convolution and Filtering 95 4.2.2 Filter Separability 97 4.3 Discrete Averaging 99 4.3.1 Gaussian Filter 100 4.3.2 Binomial Filter 101 4.4 Discrete Differentiation 105 4.4.1 Optimized Differentiating Filters 105 4.4.2 Savitzky–Golay Filters 108 4.5 Edge Detection 115 4.5.1 Edges from Signal Gradient 117 4.5.2 Edges from the Savitzky–Golay Filter 119 4.5.3 Laplacian of Gaussian 120 4.5.4 Difference of Gaussians 126 4.5.5 Morphological Edge Detector 127 4.6 Structural Tensor 127 4.6.1 Locally Oriented Neighbourhoods in Images 128 4.6.2 Tensor Representation of Local Neighbourhoods 133 4.6.3 Multichannel Image Processing with Structural Tensor 143 4.7 Corner Detection 144 4.7.1 The Most Common Corner Detectors 144 4.7.2 Corner Detection with the Structural Tensor 149 4.8 Practical Examples 151 4.8.1 C++ Implementations 151 4.8.2 Implementation of the Morphological Operators 157 4.8.3 Examples in Matlab: Computation of the SVD 161 4.9 Closure 162 4.9.1 Further Reading 163 4.9.2 Problems and Exercises 163 5 Scale-space Vision 165 5.1 Abstract 165 5.2 Basic Concepts 165 5.2.1 Context 165 5.2.2 Image Scale 166 5.2.3 Image Matching Over Scale 166 5.3 Constructing a Scale-space 168 5.3.1 Gaussian Scale-space 168 5.3.2 Differential Scale-space 170 5.4 Multi-resolution Pyramids 172 5.4.1 Introducing Multi-resolution Pyramids 172 5.4.2 How to Build Pyramids 175 5.4.3 Constructing Regular Gaussian Pyramids 175 5.4.4 Laplacian of Gaussian Pyramids 177 5.4.5 Expanding Pyramid Levels 178 5.4.6 Semi-pyramids 179 5.5 Practical Examples 181 5.5.1 C++ Examples 181 5.5.2 Matlab Examples 186 5.6 Closure 191 5.6.1 Chapter Summary 191 5.6.2 Further Reading 191 5.6.3 Problems and Exercises 192 6 Image Matching Algorithms 193 6.1 Abstract 193 6.2 Basic Concepts 193 6.3 Match Measures 194 6.3.1 Distances of Image Regions 194 6.3.2 Matching Distances for Bit Strings 198 6.3.3 Matching Distances for Multichannel Images 199 6.3.4 Measures Based on Theory of Information 202 6.3.5 Histogram Matching 205 6.3.6 Efficient Computations of Distances 206 6.3.7 Nonparametric Image Transformations 209 6.3.8 Log-polar Transformation for Image Matching 218 6.4 Computational Aspects of Matching 222 6.4.1 Occlusions 222 6.4.2 Disparity Estimation with Subpixel Accuracy 224 6.4.3 Evaluation Methods for Stereo Algorithms 226 6.5 Diversity of Stereo Matching Methods 229 6.5.1 Structure of Stereo Matching Algorithms 233 6.6 Area-based Matching 238 6.6.1 Basic Search Approach 239 6.6.2 Interpreting Match Cost 241 6.6.3 Point-oriented Implementation 245 6.6.4 Disparity-oriented Implementation 250 6.6.5 Complexity of Area-based Matching 256 6.6.6 Disparity Map Cross-checking 257 6.6.7 Area-based Matching in Practice 259 6.7 Area-based Elastic Matching 273 6.7.1 Elastic Matching at a Single Scale 273 6.7.2 Elastic Matching Concept 278 6.7.3 Scale-based Search 280 6.7.4 Coarse-to-fine Matching Over Scale 283 6.7.5 Scale Subdivision 284 6.7.6 Confidence Over Scale 285 6.7.7 Final Multi-resolution Matcher 286 6.8 Feature-based Image Matching 288 6.8.1 Zero-crossing Matching 289 6.8.2 Corner-based Matching 292 6.8.3 Edge-based Matching: The Shirai Method 295 6.9 Gradient-based Matching 296 6.10 Method of Dynamic Programming 298 6.10.1 Dynamic Programming Formulation of the Stereo Problem 301 6.11 Graph Cut Approach 306 6.11.1 Graph Cut Algorithm 306 6.11.2 Stereo as a Voxel Labelling Problem 311 6.11.3 Stereo as a Pixel Labelling Problem 312 6.12 Optical Flow 314 6.13 Practical Examples 318 6.13.1 Stereo Matching Hierarchy in C++ 318 6.13.2 Log-polar Transformation 319 6.14 Closure 321 6.14.1 Further Reading 321 6.14.2 Problems and Exercises 322 7 Space Reconstruction and Multiview Integration 323 7.1 Abstract 323 7.2 General 3D Reconstruction 323 7.2.1 Triangulation 324 7.2.2 Reconstruction up to a Scale 325 7.2.3 Reconstruction up to a Projective Transformation 327 7.3 Multiview Integration 329 7.3.1 Implicit Surfaces and Marching Cubes 330 7.3.2 Direct Mesh Integration 338 7.4 Closure 342 7.4.1 Further Reading 342 8 Case Examples 343 8.1 Abstract 343 8.2 3D System for Vision-Impaired Persons 343 8.3 Face and Body Modelling 345 8.3.1 Development of Face and Body Capture Systems 345 8.3.2 Imaging Resolution, 3D Resolution and Implications for Applications 346 8.3.3 3D Capture and Analysis Pipeline for Constructing Virtual Humans 350 8.4 Clinical and Veterinary Applications 352 8.4.1 Development of 3D Clinical Photography 352 8.4.2 Clinical Requirements for 3D Imaging 353 8.4.3 Clinical Assessment Based on 3D Surface Anatomy 353 8.4.4 Extraction of Basic 3D Anatomic Measurements 354 8.4.5 Vector Field Surface Analysis by Means of Dense Correspondences 357 8.4.6 Eigenspace Methods 359 8.4.7 Clinical and Veterinary Examples 362 8.4.8 Multimodal 3D Imaging 367 8.5 Movie Restoration 370 8.6 Closure 374 8.6.1 Further Reading 374 Part III 375 9 Basics of the Projective Geometry 377 9.1 Abstract 377 9.2 Homogeneous Coordinates 377 9.3 Point, Line and the Rule of Duality 379 9.4 Point and Line at Infinity 380 9.5 Basics on Conics 382 9.5.1 Conics in ℘2 382 9.5.2 Conics in ℘2 384 9.6 Group of Projective Transformations 385 9.6.1 Projective Base 385 9.6.2 Hyperplanes 386 9.6.3 Projective Homographies 386 9.7 Projective Invariants 387 9.8 Closure 388 9.8.1 Further Reading 389 10 Basics of Tensor Calculus for Image Processing 391 10.1 Abstract 391 10.2 Basic Concepts 391 10.2.1 Linear Operators 392 10.2.2 Change of Coordinate Systems: Jacobians 393 10.3 Change of a Base 394 10.4 Laws of Tensor Transformations 396 10.5 The Metric Tensor 397 10.5.1 Covariant and Contravariant Components in a Curvilinear Coordinate System 397 10.5.2 The First Fundamental Form 399 10.6 Simple Tensor Algebra 399 10.6.1 Tensor Summation 399 10.6.2 Tensor Product 400 10.6.3 Contraction and Tensor Inner Product 400 10.6.4 Reduction to Principal Axes 400 10.6.5 Tensor Invariants 401 10.7 Closure 401 10.7.1 Further Reading 401 11 Distortions and Noise in Images 403 11.1 Abstract 403 11.2 Types and Models of Noise 403 11.3 Generating Noisy Test Images 405 11.4 Generating Random Numbers with Normal Distributions 407 11.5 Closure 408 11.5.1 Further Reading 408 12 Image Warping Procedures 409 12.1 Abstract 409 12.2 Architecture of the Warping System 409 12.3 Coordinate Transformation Module 410 12.3.1 Projective and Affine Transformations of a Plane 410 12.3.2 Polynomial Transformations 411 12.3.3 Generic Coordinates Mapping 412 12.4 Interpolation of Pixel Values 412 12.4.1 Bilinear Interpolation 412 12.4.2 Interpolation of Nonscalar-Valued Pixels 414 12.5 The Warp Engine 414 12.6 Software Model of the Warping Schemes 415 12.6.1 Coordinate Transformation Hierarchy 415 12.6.2 Interpolation Hierarchy 416 12.6.3 Image Warp Hierarchy 416 12.7 Warp Examples 419 12.8 Finding the Linear Transformation from Point Correspondences 420 12.8.1 Linear Algebra on Images 424 12.9 Closure 427 12.9.1 Further Reading 428 13 Programming Techniques for Image Processing and Computer Vision 429 13.1 Abstract 429 13.2 Useful Techniques and Methodology 430 13.2.1 Design and Implementation 430 13.2.2 Template Classes 436 13.2.3 Asserting Code Correctness 438 13.2.4 Debugging Issues 440 13.3 Design Patterns 441 13.3.1 Template Function Objects 441 13.3.2 Handle-body or Bridge 442 13.3.3 Composite 445 13.3.4 Strategy 447 13.3.5 Class Policies and Traits 448 13.3.6 Singleton 450 13.3.7 Proxy 450 13.3.8 Factory Method 451 13.3.9 Prototype 452 13.4 Object Lifetime and Memory Management 453 13.5 Image Processing Platforms 455 13.5.1 Image Processing Libraries 455 13.5.2 Writing Software for Different Platforms 455 13.6 Closure 456 13.6.1 Further Reading 456 14 Image Processing Library 457 References 459 Index 475

Read more less

Book SynopsisThis book is a collection representing some of the most powerful and useful computer techniques in the service of art.Table of ContentsList of Figures xxi List of Tables xlv List of Algorithms xlvii Preface xlix Lorenzo Lotto lviii Giovanni Morelli and the birth of "scientific" connoisseurship lix Overview lxi Intended audience lxii Prerequisites lxiii Acknowledgements lxiv 1 Digital imaging 1 1.1 Introduction 1 1.2 Electromagnetic radiation and light 4 1.3 Interaction of electromagnetic radiation with art materials 7 1.4 Cameras and scanners 9 1.4.1 Cameras 10 1.4.2 Flatbed scanners 11 1.5 Parameters for image acquisition in the visible 12 Billy Pappas 13 1.5.1 Spatial resolution 15 1.5.2 Bit depth 16 1.5.3 Dynamic range and contrast 17 1.6 Reading digital images of art on–screen 18 1.6.1 Reading a digital image of Leonardo's La Bella Principessa 22 Leonardo da Vinci 22 1.7 Infrared photography and reflectography 25 1.8 Ultraviolet imaging 26 1.9 Multispectral and hyperspectral imaging 27 1.9.1 Hyperspectral imaging of the Archimedes Palimpsest 30 1.10 X-radiographic imaging 32 1.11 Fluorescence imaging 35 1.12 Capture of three–dimensional surfaces of art 37 1.12.1 Raking illumination 38 1.12.2 Reflectance transformation imaging (RTI) 40 1.12.3 Stereographic imaging 42 1.13 Optical coherence tomography (OCT) 43 1.14 Raman spectroscopic imaging and X-ray fluorescence imaging 45 1.14.1 Raman spectroscopic imaging (RSI) 45 1.14.2 X-ray fluorescence imaging (XRF) 46 1.15 Summary 47 1.16 Bibliographical remarks 49 2 Image processing 53 2.1 Introduction 53 2.2 Pixel–based image processing 57 2.3 Region–based image processing 61 2.3.1 Linear image processing 62 2.3.2 Nonlinear region–based image processing 63 2.3.3 Color quantization 64 2.3.4 Edge and line detection 69 2.3.5 Dilation and erosion 71 2.3.6 Skeletonization 72 2.4 Inpainting 72 2.5 Feature extraction 74 2.5.1 Keypoint extraction 75 2.5.2 Craquelure and crazing analysis 78 2.5.3 Computational tests for counterproofing by Jan van der Heyden 81 Jan van der Heyden 83 2.6 Segmentation 86 2.6.1 Deep nets for image segmentation 88 2.7 Geometric transformations 95 2.8 Chamfer transform and Chamfer distance 101 2.8.1 Tests for copying of Jan van Eyck's portraits of Niccolò Albergati 103 2.9 Discrete Fourier and wavelet transforms 111 2.9.1 Discrete Fourier transform (DFT) 111 2.9.2 Canvas support weave analysis 114 2.9.3 Discrete wavelet transform (DWT) 116 2.10 Compositing and integrating art images 118 2.10.1 Image compositing 118 2.10.2 Superresolution 119 2.11 Image separation 123 2.12 Summary 123 2.13 Bibliographical remarks 125 3 Color analysis 129 3.1 Introduction 129 3.2 Visible–light spectra and color appearance 132 3.3 Overview of human color vision 133 3.3.1 Properties of color descriptions 134 3.3.2 Opponent color processing and unique hues 137 3.3.3 Humanist descriptions of color 138 3.3.4 Spatial aspects of color perception 139 Josef Albers 140 3.3.5 Color and lightness constancy and brightness perception 141 3.3.6 Quantitative descriptions and additive color mixing 141 3.3.7 Representing artists' palettes 145 3.4 Physics of color in art materials 147 3.4.1 Pigments and color appearance 147 3.5 Representing color arising from mixing paints 151 3.5.1 Identifying pigments in artworks based on spectra 152 3.6 Digital rejuvenation of pigment colors 154 3.6.1 Digital rejuvenation of faded artworks 157 Georges Seurat 158 3.7 Digital cleaning of paintings 160 3.8 Summary 164 3.9 Bibliographical remarks 165 4 Brush stroke and mark analysis 171 4.1 Introduction 171 Cy Twombly 173 4.2 Analysis of printed lines and marks 175 Katsushika Hokusai 178 4.3 Inferring tools from marks 182 Sheila Waters 184 4.3.1 Analysis of brush strokes 185 4.3.2 Segmenting and isolating brush strokes computationally 187 4.3.3 Extracting opaque marks in multiple layers 189 Vincent Willem van Gogh 193 4.3.4 Visual evidence of authorship of Pollock's drip paintings 194 Jackson Pollock 195 4.3.5 Extracting layers of translucent brush strokes 195 4.4 Characterizing the shapes of strokes and marks 203 4.5 Global methods for inferring sequences of marks in paintings 206 4.6 Summary 208 4.7 Bibliographical remarks 208 5 Perspective and geometric analysis 211 5.1 Introduction 211 5.2 Projective geometry 214 5.2.1 The mathematics of projection 216 5.2.2 One–point, two–point, and three–point perspectives 222 5.2.3 Parallel or orthographic perspective in Asian art 223 5.3 Estimating the center of projection 224 5.3.1 Foreshortening and size comparisons of depicted objects 230 Piero della Francesca 231 5.3.2 Cross–ratio analysis 232 5.3.3 Estimating the center of projection from object sizes 234 5.4 Estimating geometric accuracy in artworks 235 5.4.1 Hans Memling's Flower Still-Life 235 Hans Memling 237 5.4.2 The carpet in Lorenzo Lotto's Husband and Wife 238 5.4.3 The chandelier in the Arnolfini Portrait 238 Jan van Eyck 243 5.4.4 Warping Andrea Mantegna's Lamentation of Christ to make consistent perspective 251 5.4.5 Dewarping the murals in Sennedjem's Tomb 252 5.4.6 Warping de Chirico's Ariadne to make consistent perspective 255 Giorgio de Chirico 256 5.4.7 Robert Campin and workshop's Mérode Altarpiece 257 Robert Campin 258 5.5 Slant anamorphic art 260 Ed Ruscha (Edward Joseph Ruscha IV) 260 5.5.1 Hans Holbein's The Ambassadors 263 Hans Holbein 263 5.6 Inferring depth from projected images 264 5.6.1 Computing a three–dimensional model from one perspective image 265 Masaccio 266 5.6.2 Computing a three–dimensional model from two perspective images 267 5.7 Summary 271 5.8 Bibliographical remarks 272 6 Optical analysis 275 6.1 Introduction 275 6.2 Reflection and refraction 277 6.3 Plane mirrors 278 6.3.1 Virtual image formation by plane mirrors 279 6.3.2 Depictions of plane mirrors in art 281 6.3.3 Diego Velázquez’s Las Meninas 283 Diego Velázquez 284 6.4 Convex spherical mirrors 288 6.4.1 Virtual image formation by convex spherical mirrors 290 6.4.2 Jan van Eyck’s Portrait of Giovanni Arnolfini and his Wife 292 6.4.3 Claude glass 297 6.4.4 Parmigianino’s Self–Portrait in a Convex Mirror 298 Parmigianino (Girolamo Francesco Maria Mazzola) 298 6.4.5 Hans Memling's Virgin and Child and Maarten van Nieuwenhove 304 6.4.6 Dewarping images in generalized cylindrical mirrors 308 6.5 Conical and cylindrical mirrors and anamorphic art 312 6.5.1 Conical mirror anamorphic art 313 6.5.2 Cylindrical mirror anamorphic art 317 6.6 Concave spherical mirrors 318 6.6.1 Virtual image formation by concave mirrors 320 6.6.2 Real image formation by concave mirrors 322 6.7 Converging lenses 323 6.7.1 Virtual image formation by converging lenses 325 6.7.2 Real image formation by convex lenses 327 6.8 Camera lucida and camera obscura 328 6.8.1 Camera lucida 328 6.8.2 Camera obscura 331 6.8.3 Depth of field, depth of focus, and blur spots 333 6.9 Optical projections and the creation of art 336 6.9.1 Jan van Eyck's Portrait of Giovanni Arnolfini and his wife 337 6.9.2 Caravaggio's Supper at Emmaus 342 6.9.3 Lorenzo Lotto's Husband and Wife 345 6.9.4 Johannes Vermeer's Lady at the Virginals with a Gentleman 349 Johannes Vermeer 349 6.9.5 Canaletto's Piazza San Marco 363 Canaletto (Giovanni Antonio Canal) 364 6.9.6 Photorealists 364 Philip Barlow 366 6.10 Refraction and nonimaging optics in art 366 6.10.1 Leonardo's Salvator Mundi 366 6.11 Summary 371 6.12 Bibliographical remarks 372 7 Lighting analysis 377 7.1 Introduction 377 7.2 Basic shadows 381 7.2.1 General classes of lighting analysis methods 383 7.3 Cast–shadow analysis 383 7.3.1 Illumination from two or more point-sources 388 7.3.2 Cast–shadow analysis under geometric constraints 388 7.4 Lighting information from highlights 389 7.4.1 Illumination direction from highlights on simple estimated shapes 393 7.5 The optics of diffuse reflections 394 7.6 Inferring illumination from plane surfaces 396 Georges de la Tour 398 7.7 Interreflection 400 7.8 Occluding–contour algorithms 401 7.8.1 Single–point occluding–contour algorithm 403 7.8.2 General occluding–contour algorithm 405 Caravaggio (Michelangelo Merisi da Caravaggio) 407 7.8.3 Lightfield occluding–contour algorithm 408 Garth Herrick 409 7.8.4 Theory of the lightfield occluding–contour algorithm 410 7.8.5 Application of the lightfield occluding–contour algorithm 415 7.9 Computer graphics for the analysis of lighting 418 7.9.1 Georges de la Tour's Christ in the Carpenter's Studio (model) 419 7.9.2 Johannes Vermeer's Girl with a Pearl Earring 421 7.9.3 René Magritte's The Menaced Assassin 422 7.9.4 Bidirectional reflectance distribution functions (BRDFs) 424 7.9.5 Caravaggio's The Calling of St. Matthew 425 7.10 Shape–from–shading algorithms 426 7.10.1 Shape–from–shading by deep neural networks 429 7.10.2 Shape–from–shading for estimating both illumination and depth 430 7.11 Integrating lighting estimates 433 7.11.1 Integrating one–dimensional lighting estimates 433 7.11.2 Integrating two–dimensional lighting estimates 436 7.12 Lighting analysis for dating depicted scenes 439 7.13 Summary 442 7.14 Bibliographical remarks 444 8 Object analysis 449 8.1 Introduction 449 8.2 Image–based object classification 452 8.2.1 Feature–based object recognition 452 8.3 Feature–based analysis of faces and bodies 454 8.3.1 Feature–based analysis of body pose 464 8.3.2 Feature–based analysis of head poses 466 8.4 Deep neural network–based object recognition 468 Jacques-Louis David 472 8.4.1 Transfer training 472 8.5 Summary 474 8.6 Bibliographical remarks 475 9 Style and composition analysis 477 9.1 Introduction 477 9.2 Automatic classification of style 480 9.3 Compositional balance 482 9.3.1 Computational balance of actors 485 9.4 Geometric properties of composition 486 9.4.1 Design in Piet Mondrian's Neoplastic paintings 487 Piet Mondrian 487 9.5 Analysis of trends and similarities in artistic style 497 9.5.1 Trends in landscape compositions 498 9.5.2 Large–scale trends in the development of style 502 9.5.3 Graph representations of stylistic similarities 503 9.6 Style transfer 505 9.6.1 Style transfer by deep networks 505 9.6.2 Rejuvenating tapestries 506 9.6.3 Coloration of black–and–white photographs of artworks 507 9.6.4 Style transfer for visualizing underdrawings 509 9.7 Recovering Rembrandt's complete The Night Watch 513 Rembrandt 514 9.8 Computational generation of images for art analysis 516 9.8.1 Computational recovery of lost artworks 518 9.9 Summary 521 9.10 Bibliographical remarks 522 10 Semantic analysis 525 10.1 Introduction 525 Jacques-Louis David 528 10.2 Semantics and visual art 534 10.2.1 Natural language processing and knowledge representation 536 10.3 Meaning through associations 538 10.3.1 Signifiers and signifieds 538 10.4 Semantics of color 544 10.5 Identifying saints by their attributes 546 Andrea del Verrocchio 549 10.6 Learning associations between signifiers and signifieds 550 Harmen Steenwijck 551 10.7 Meaning through artistic style 554 10.7.1 Context in the creation of meaning 556 10.8 Automatic image captioning and question answering 557 10.8.1 Image captioning 557 10.8.2 Automatic answering of questions about artworks 559 10.9 Meaning through shape relations and associations 563 Rogier van der Weyden 563 10.9.1 Recognizing meaning–bearing stories 565 Albrecht Dürer 567 10.10 Summary 568 10.11 Bibliographical remarks 569 Appendix 573 A Symbols, acronyms, and mathematical notation 573 A.1 Mathematical notation, definitions, and operations 573 A.2 Solving simultaneous linear equations 578 A.3 Lagrange optimization 579 A.4 Basis functions 580 A.5 Discrete Fourier analysis and synthesis 580 A.6 Discrete wavelet transform 582 A.7 Spherical harmonics 582 B Probability 584 B.1 Accuracy, precision, and recall 585 B.2 Conditional probability 585 B.3 The definition of information 586 B.4 Hidden Markov models (HMMs) 586 C Bayes' theorem and reasoning about uncertainty 588 C.1 Statistical independence 588 C.2 Maximum likelihood estimation 589 C.3 Bias and variance 591 C.4 Intersection over Union metric 592 D Deep neural networks 593 E Ray tracing and image formation in mirrors and lenses 596 E.1 Converging lenses 596 E.2 Diverging lenses 599 E.3 Mirrors 600 E.4 The focal length and radius of curvature of a spherical mirror 602 E.5 Spherical versus parabolic mirrors 603 F Resources 604 Epilog 607 Glossary 609 Bibliography 615 Figure credits 673 Timeline of artists 682 Index of artists 683 Index 687 About the book 713

Read more less

Book SynopsisIntelligent Image Processing describes the EyeTap technology that allows non-invasive tapping into the human eye through devices built into eyeglass frames. This isn't merely about a computer screen inside eyeglasses, but rather the ability to have a shared telepathic experience among viewers.Table of ContentsPreface 1 Humanistic Intelligence as a Basis for Intelligent Image Processing 1.1 Humanistic Intelligence/ 1.2 "WearComp" as Means of Realizing Humanistic Intelligence 1.3 Practical Embodiments of Humanistic Intelligence 2 Where on the Body is the Best Place for a Personal Imaging System? 2.1 Portable Imaging Systems 2.2 Personal Handheld Systems 2.3 Concomitant Cover Activities and the Videoclips Camera System 2.4 The Wristwatch Videophone: A Fully Functional "Always Ready" Prototype 2.5 Telepointer: Wearable Hands-Free Completely Self-Contained Visual Augmented Reality 2.6 Portable Personal Pulse Doppler Radar Vision System Based on Time-Frequency Analysis and q-Chirplet Transform 2.7 When Both Camera and Display are Headworn: Personal Imaging and Mediated Reality 2.8 Partially Mediated Reality 2.9 Seeing "Eye-to-Eye" 2.10 Exercises, Problem Sets, and Homework 3 The EyeTap Principle: Effectively Locating the Camera Inside the Eye as an Alternative to Wearable Camera Systems 3.1 A Personal Imaging System for Lifelong Video Capture 3.2 The EyeTap Principle 3.3 Practical Embodiments of EyeTap 3.4 Problems with Previously Known Camera Viewfinders 3.5 The Aremac 3.6 The Foveated Personal Imaging System 3.7 Teaching the EyeTap Principle 3.8 Calibration of EyeTap Systems 3.9 Using the Device as a Reality Mediator 3.10 User Studies 3.11 Summary and Conclusions 3.12 Exercises, Problem Sets, and Homework 4 Comparametric Equations, Quantigraphic Image Processing, and Comparagraphic Rendering 4.1 Historical Background 4.2 The Wyckoff Principle and the Range of Light 4.3 Comparametric Image Processing: Comparing Differently Exposed Images of the Same Subject Matter 4.4 The Comparagram: Practical Implementations of Comparanalysis 4.5 Spatiotonal Photoquantigraphic Filters 4.6 Glossary of Functions 4.7 Exercises, Problem Sets, and Homework 5 Lightspace and Antihomomorphic Vector Spaces 5.1 Lightspace 5.2 The Lightspace Analysis Function 5.3 The "Spotflash" Primitive 5.4 LAF×LSF Imaging ("Lightspace") 5.5 Lightspace Subspaces 5.6 "Lightvector" Subspace 5.7 Painting with Lightvectors: Photographic/Videographic Origins and Applications of WearComp-Based Mediated Reality 5.8 Collaborative Mediated Reality Field Trials 5.9 Conclusions 5.10 Exercises, Problem Sets, and Homework 6 VideoOrbits: The Projective Geometry Renaissance 6.1 VideoOrbits 6.2 Background 6.3 Framework: Motion Parameter Estimation and Optical Flow 6.4 Multiscale Implementations in 2-D 6.5 Performance and Applications 6.6 AGC and the Range of Light 6.7 Joint Estimation of Both Domain and Range Coordinate Transformations 6.8 The Big Picture 6.9 Reality Window Manager 6.10 Application of Orbits: The Photonic Firewall 6.11 All the World's a Skinner Box 6.12 Blocking Spam with a Photonic Filter 6.13 Exercises, Problem Sets, and Homework Appendix A: Safety First! Appendix B: Multiambic Keyer for Use While Engaged in Other Activities B.1 Introduction B.2 Background and Terminology on Keyers B.3 Optimal Keyer Design: The Conformal Keyer B.4 The Seven Stages of a Keypress B.5 The Pentakeyer B.6 Redundancy B.7 Ordinally Conditional Modifiers B.8 Rollover B.8.1 Example of Rollover on a Cybernetic Keyer B.9 Further Increasing the Chordic Redundancy Factor: A More Expressive Keyer B.10 Including One Time Constant B.11 Making a Conformal Multiambic Keyer B.12 Comparison to Related Work B.13 Conclusion B.14 Acknowledgments Appendix C: WearCam GNUX Howto C.1 Installing GNUX on WearComps C.2 Getting Started C.3 Stop the Virus from Running C.4 Making Room for an Operating System C.5 Other Needed Files C.6 Defrag / 323 C.7 Fips C.8 Starting Up in GNUX with Ramdisk Appendix D: How to Build a Covert Computer Imaging System into Ordinary Looking Sunglasses D.1 The Move from Sixth-Generation WearComp to Seventh-Generation D.2 Label the Wires! D.3 Soldering Wires Directly to the Kopin CyberDisplay D.4 Completing the Computershades Bibliography Index

Read more less

Book SynopsisIn recent years there have been major advances in understanding visual processing. This work brings together experts from various disciplines, ranging from computer science to neuropsychology, to discuss how the work carried out in their field fits into the broader context of vision research.Table of ContentsContructing the perception of surfaces from multiple cues, Kent A. Stevens' visual analysis and representation of spatial relations, Roger J. Watt; modern theories of Gestalt perception, Stephen J. Palmer; thinking visually, Kris N. Kirby and Stephen M. Kosslyn; perceiving and recognizing faces, Vicki Bruce; the breakdown approach to visual perception - neuropsychological studies of object recognition, Glyn W. Humphreys et al; mechanisms which mediate discrimination of 2-D spatial patterns in distributed images, Keith H. Ruddock; the analysis of 3-D shape - psychological principles and neural mechanisms, Andrew J. Parker et al; identification of disoriented objects - a dual-systems theory, Pierre Jolicoeur; surface layout from retinal flow, Mike Harris et al; neural facades - visual representations of static and moving form-and-colour-and-depth, Stephen Grossberg.

Read more less

Book SynopsisThe volumes in this series contain studies in computational vision or image understanding, and explain the computations that underlie the extraction and use of visual information by both biological and artificial systems. Reprints of seminal studies are included along with the original articles.

Read more less

Book SynopsisThis comprehensive textbook presents a broad review of both traditional (i.e., conventional) and deep learning aspects of object detection in various adversarial real-world conditions in a clear, insightful, and highly comprehensive style. Beginning with the relation of computer vision and object detection, the text covers the various representation ofobjects, applications of object detection, and real-world challenges faced by the research community for object detection task. The book addresses various real-world degradations and artifacts for the object detection task and also highlights the impacts of artifacts in the object detection problems. The book covers various imaging modalities and benchmark datasets mostly adopted by the research community for solving various aspects of object detection tasks. The book also collects together solutions and perspectives proposed by the preeminent researchers in the field, addressing not only the background of visibility enhancement

Read more less

Book SynopsisExplainability methods provide an essential toolkit for better understanding model behavior, and this practical guide brings together best-in-class techniques for model explainability.

Read more less

Book SynopsisThis practical guide gives engineering professionals, including product managers and technology leaders, an end-to-end framework for solving real-world industrial, commercial, and scientific problems with edge AI.

Read more less

Book SynopsisExplains the theory behind basic computer vision and provides a bridge from the theory to practical implementation using the industry standard OpenCV libraries Computer Vision is a rapidly expanding area and it is becoming progressively easier for developers to make use of this field due to the ready availability of high quality libraries (such as OpenCV 2). This text is intended to facilitate the practical use of computer vision with the goal being to bridge the gap between the theory and the practical implementation of computer vision. The book will explain how to use the relevant OpenCV library routines and will be accompanied by a full working program including the code snippets from the text. This textbook is a heavily illustrated, practical introduction to an exciting field, the applications of which are becoming almost ubiquitous. We are now surrounded by cameras, for example cameras on computers & tablets/ cameras built into our mobile phones/ cameras in games Trade Review“Although there are many computer vision books on the market that offer a more comprehensive approach to explaining the computer vision concepts, extremely few offer such comprehensive practical examples. In this context, the book would be very welcome by beginner code developers." (Computing Reviews, 8 August 2014) Table of ContentsPreface xiii 1 Introduction 1 1.1 A Difficult Problem 1 1.2 The Human Vision System 2 1.3 Practical Applications of Computer Vision 3 1.4 The Future of Computer Vision 5 1.5 Material in This Textbook 6 1.6 Going Further with Computer Vision 7 2 Images 9 2.1 Cameras 9 2.1.1 The Simple Pinhole Camera Model 9 2.2 Images 10 2.2.1 Sampling 11 2.2.2 Quantisation 11 2.3 Colour Images 13 2.3.1 Red–Green–Blue (RGB) Images 14 2.3.2 Cyan–Magenta–Yellow (CMY) Images 17 2.3.3 YUV Images 17 2.3.4 Hue Luminance Saturation (HLS) Images 18 2.3.5 Other Colour Spaces 20 2.3.6 Some Colour Applications 20 2.4 Noise 22 2.4.1 Types of Noise 23 2.4.2 Noise Models 25 2.4.3 Noise Generation 26 2.4.4 Noise Evaluation 26 2.5 Smoothing 27 2.5.1 Image Averaging 27 2.5.2 Local Averaging and Gaussian Smoothing 28 2.5.3 Rotating Mask 30 2.5.4 Median Filter 31 3 Histograms 35 3.1 1D Histograms 35 3.1.1 Histogram Smoothing 36 3.1.2 Colour Histograms 37 3.2 3D Histograms 39 3.3 Histogram/Image Equalisation 40 3.4 Histogram Comparison 41 3.5 Back-projection 43 3.6 k-means Clustering 44 4 Binary Vision 49 4.1 Thresholding 49 4.1.1 Thresholding Problems 50 4.2 Threshold Detection Methods 51 4.2.1 Bimodal Histogram Analysis 52 4.2.2 Optimal Thresholding 52 4.2.3 Otsu Thresholding 54 4.3 Variations on Thresholding 56 4.3.1 Adaptive Thresholding 56 4.3.2 Band Thresholding 57 4.3.3 Semi-thresholding 58 4.3.4 Multispectral Thresholding 58 4.4 Mathematical Morphology 59 4.4.1 Dilation 60 4.4.2 Erosion 62 4.4.3 Opening and Closing 63 4.4.4 Grey-scale and Colour Morphology 65 4.5 Connectivity 66 4.5.1 Connectedness: Paradoxes and Solutions 66 4.5.2 Connected Components Analysis 67 5 Geometric Transformations 71 5.1 Problem Specification and Algorithm 71 5.2 Affine Transformations 73 5.2.1 Known Affine Transformations 74 5.2.2 Unknown Affine Transformations 75 5.3 Perspective Transformations 76 5.4 Specification of More Complex Transformations 78 5.5 Interpolation 78 5.5.1 Nearest Neighbour Interpolation 79 5.5.2 Bilinear Interpolation 79 5.5.3 Bi-Cubic Interpolation 80 5.6 Modelling and Removing Distortion from Cameras 80 5.6.1 Camera Distortions 81 5.6.2 Camera Calibration and Removing Distortion 82 6 Edges 83 6.1 Edge Detection 83 6.1.1 First Derivative Edge Detectors 85 6.1.2 Second Derivative Edge Detectors 92 6.1.3 Multispectral Edge Detection 97 6.1.4 Image Sharpening 98 6.2 Contour Segmentation 99 6.2.1 Basic Representations of Edge Data 99 6.2.2 Border Detection 102 6.2.3 Extracting Line Segment Representations of Edge Contours 105 6.3 Hough Transform 108 6.3.1 Hough for Lines 109 6.3.2 Hough for Circles 111 6.3.3 Generalised Hough 112 7 Features 115 7.1 Moravec Corner Detection 117 7.2 Harris Corner Detection 118 7.3 FAST Corner Detection 121 7.4 SIFT 122 7.4.1 Scale Space Extrema Detection 123 7.4.2 Accurate Keypoint Location 124 7.4.3 Keypoint Orientation Assignment 126 7.4.4 Keypoint Descriptor 127 7.4.5 Matching Keypoints 127 7.4.6 Recognition 127 7.5 Other Detectors 129 7.5.1 Minimum Eigenvalues 130 7.5.2 SURF 130 8 Recognition 131 8.1 Template Matching 131 8.1.1 Applications 131 8.1.2 Template Matching Algorithm 133 8.1.3 Matching Metrics 134 8.1.4 Finding Local Maxima or Minima 135 8.1.5 Control Strategies for Matching 137 8.2 Chamfer Matching 137 8.2.1 Chamfering Algorithm 137 8.2.2 Chamfer Matching Algorithm 139 8.3 Statistical Pattern Recognition 140 8.3.1 Probability Review 142 8.3.2 Sample Features 143 8.3.3 Statistical Pattern Recognition Technique 149 8.4 Cascade of Haar Classifiers 152 8.4.1 Features 154 8.4.2 Training 156 8.4.3 Classifiers 156 8.4.4 Recognition 158 8.5 Other Recognition Techniques 158 8.5.1 Support Vector Machines (SVM) 158 8.5.2 Histogram of Oriented Gradients (HoG) 159 8.6 Performance 160 8.6.1 Image and Video Datasets 160 8.6.2 Ground Truth 161 8.6.3 Metrics for Assessing Classification Performance 162 8.6.4 Improving Computation Time 165 9 Video 167 9.1 Moving Object Detection 167 9.1.1 Object of Interest 168 9.1.2 Common Problems 168 9.1.3 Difference Images 169 9.1.4 Background Models 171 9.1.5 Shadow Detection 179 9.2 Tracking 180 9.2.1 Exhaustive Search 181 9.2.2 Mean Shift 181 9.2.3 Dense Optical Flow 182 9.2.4 Feature Based Optical Flow 185 9.3 Performance 186 9.3.1 Video Datasets (and Formats) 186 9.3.2 Metrics for Assessing Video Tracking Performance 187 10 Vision Problems 189 10.1 Baby Food 189 10.2 Labels on Glue 190 10.3 O-rings 191 10.4 Staying in Lane 192 10.5 Reading Notices 193 10.6 Mailboxes 194 10.7 Abandoned and Removed Object Detection 195 10.8 Surveillance 196 10.9 Traffic Lights 197 10.10 Real Time Face Tracking 198 10.11 Playing Pool 199 10.12 Open Windows 200 10.13 Modelling Doors 201 10.14 Determining the Time from Analogue Clocks 202 10.15 Which Page 203 10.16 Nut/Bolt/Washer Classification 204 10.17 Road Sign Recognition 205 10.18 License Plates 206 10.19 Counting Bicycles 207 10.20 Recognise Paintings 208 References 209 Index 213

Read more less

Book SynopsisComputer Vision in Vehicle Technology: Land, Sea & Air Antonio M. Lopez, Universitat Autonoma de Barcelona, Spain Atsushi Imiya, Chiba University, Japan Tomas Pajdla, Czech Technical University, Prague Jose M.Table of ContentsList of Contributors ix Preface xi Abbreviations and Acronyms xiii 1 Computer Vision in Vehicles 1Reinhard Klette 1.1 Adaptive Computer Vision for Vehicles 1 1.1.1 Applications 1 1.1.2 Traffic Safety and Comfort 2 1.1.3 Strengths of (Computer) Vision 2 1.1.4 Generic and Specific Tasks 3 1.1.5 Multi-module Solutions 4 1.1.6 Accuracy, Precision, and Robustness 5 1.1.7 Comparative Performance Evaluation 5 1.1.8 There Are Many Winners 6 1.2 Notation and Basic Definitions 6 1.2.1 Images and Videos 6 1.2.2 Cameras 8 1.2.3 Optimization 10 1.3 Visual Tasks 12 1.3.1 Distance 12 1.3.2 Motion 16 1.3.3 Object Detection and Tracking 18 1.3.4 Semantic Segmentation 21 1.4 Concluding Remarks 23 Acknowledgments 23 2 Autonomous Driving 24Uwe Franke 2.1 Introduction 24 2.1.1 The Dream 24 2.1.2 Applications 25 2.1.3 Level of Automation 26 2.1.4 Important Research Projects 27 2.1.5 Outdoor Vision Challenges 30 2.2 Autonomous Driving in Cities 31 2.2.1 Localization 33 2.2.2 Stereo Vision-Based Perception in 3D 36 2.2.3 Object Recognition 43 2.3 Challenges 49 2.3.1 Increasing Robustness 49 2.3.2 Scene Labeling 50 2.3.3 Intention Recognition 52 2.4 Summary 52 Acknowledgments 54 3 Computer Vision for MAVs 55Friedrich Fraundorfer 3.1 Introduction 55 3.2 System and Sensors 57 3.3 Ego-Motion Estimation 58 3.3.1 State Estimation Using Inertial and Vision Measurements 58 3.3.2 MAV Pose from Monocular Vision 62 3.3.3 MAV Pose from Stereo Vision 63 3.3.4 MAV Pose from Optical Flow Measurements 65 3.4 3D Mapping 67 3.5 Autonomous Navigation 71 3.6 Scene Interpretation 72 3.7 Concluding Remarks 73 4 Exploring the Seafloor with Underwater Robots 75Rafael Garcia, Nuno Gracias, Tudor Nicosevici, Ricard Prados, Natalia Hurtos, Ricard Campos, Javier Escartin, Armagan Elibol, Ramon Hegedus and Laszlo Neumann 4.1 Introduction 75 4.2 Challenges of Underwater Imaging 77 4.3 Online Computer Vision Techniques 79 4.3.1 Dehazing 79 4.3.2 Visual Odometry 84 4.3.3 SLAM 87 4.3.4 Laser Scanning 91 4.4 Acoustic Imaging Techniques 92 4.4.1 Image Formation 92 4.4.2 Online Techniques for Acoustic Processing 95 4.5 Concluding Remarks 98 Acknowledgments 99 5 Vision-Based Advanced Driver Assistance Systems 100David Gerónimo, David Vázquez and Arturo de la Escalera 5.1 Introduction 100 5.2 Forward Assistance 101 5.2.1 Adaptive Cruise Control (ACC) and Forward Collision Avoidance (FCA) 101 5.2.2 Traffic Sign Recognition (TSR) 103 5.2.3 Traffic Jam Assist (TJA) 105 5.2.4 Vulnerable Road User Protection 106 5.2.5 Intelligent Headlamp Control 109 5.2.6 Enhanced Night Vision (Dynamic Light Spot) 110 5.2.7 Intelligent Active Suspension 111 5.3 Lateral Assistance 112 5.3.1 Lane Departure Warning (LDW) and Lane Keeping System (LKS) 112 5.3.2 Lane Change Assistance (LCA) 115 5.3.3 Parking Assistance 116 5.4 Inside Assistance 117 5.4.1 Driver Monitoring and Drowsiness Detection 117 5.5 Conclusions and Future Challenges 119 5.5.1 Robustness 119 5.5.2 Cost 121 Acknowledgments 121 6 Application Challenges from a Bird’s-Eye View 122Davide Scaramuzza 6.1 Introduction to Micro Aerial Vehicles (MAVs) 122 6.1.1 Micro Aerial Vehicles (MAVs) 122 6.1.2 Rotorcraft MAVs 123 6.2 GPS-Denied Navigation 124 6.2.1 Autonomous Navigation with Range Sensors 124 6.2.2 Autonomous Navigation with Vision Sensors 125 6.2.3 SFLY: Swarm of Micro Flying Robots 126 6.2.4 SVO, a Visual-Odometry Algorithm for MAVs 126 6.3 Applications and Challenges 127 6.3.1 Applications 127 6.3.2 Safety and Robustness 128 6.4 Conclusions 132 7 Application Challenges of Underwater Vision 133Nuno Gracias, Rafael Garcia, Ricard Campos, Natalia Hurtos, Ricard Prados, ASM Shihavuddin, Tudor Nicosevici, Armagan Elibol, Laszlo Neumann and Javier Escartin 7.1 Introduction 133 7.2 Offline Computer Vision Techniques for Underwater Mapping and Inspection 134 7.2.1 2D Mosaicing 134 7.2.2 2.5D Mapping 144 7.2.3 3D Mapping 146 7.2.4 Machine Learning for Seafloor Classification 154 7.3 Acoustic Mapping Techniques 157 7.4 Concluding Remarks 159 8 Closing Notes 161Antonio M. López References 164 Index 195

Read more less

Book SynopsisComputer Vision and Imaging in Intelligent Transportation Systems Robert P.Table of ContentsList of Contributors xiii Preface xvii Acknowledgments xxi About the Companion Website xxiii 1 Introduction 1 Raja Bala and Robert P. Loce 1.1 Law Enforcement and Security 1 1.2 Efficiency 4 1.3 Driver Safety and Comfort 5 1.4 A Computer Vision Framework for Transportation Applications 7 1.4.1 Image and Video Capture 8 1.4.2 Data Preprocessing 8 1.4.3 Feature Extraction 9 1.4.4 Inference Engine 10 1.4.5 Data Presentation and Feedback 11 Part I Imaging from the Roadway Infrastructure 15 2 Automated License Plate Recognition 17 Aaron Burry and Vladimir Kozitsky 2.1 Introduction 17 2.2 Core ALPR Technologies 18 2.2.1 License Plate Localization 19 2.2.2 Character Segmentation 24 2.2.3 Character Recognition 28 2.2.4 State Identification 38 3 Vehicle Classification 47 Shashank Deshpande, Wiktor Muron and Yang Cai 3.1 Introduction 47 3.2 Overview of the Algorithms 48 3.3 Existing AVC Methods 48 3.4 LiDAR Imaging-Based 49 3.4.1 LiDAR Sensors 49 3.4.2 Fusion of LiDAR and Vision Sensors 50 3.5 Thermal Imaging-Based 53 3.5.1 Thermal Signatures 53 3.5.2 Intensity Shape-Based 56 3.6 Shape- and Profile-Based 58 3.6.1 Silhouette Measurements 60 3.6.2 Edge-Based Classification 65 3.6.3 Histogram of Oriented Gradients 67 3.6.4 Haar Features 68 3.6.5 Principal Component Analysis 69 3.7 Intrinsic Proportion Model 72 3.8 3D Model-Based Classification 74 3.9 SIFT-Based Classification 74 3.10 Summary 75 4 Detection of Passenger Compartment Violations 81 Orhan Bulan, Beilei Xu, Robert P. Loce and Peter Paul 4.1 Introduction 81 4.2 Sensing within the Passenger Compartment 82 4.2.1 Seat Belt Usage Detection 82 4.2.2 Cell Phone Usage Detection 83 4.2.3 Occupancy Detection 83 4.3 Roadside Imaging 84 4.3.1 Image Acquisition Setup 84 4.3.2 Image Classification Methods 85 4.3.3 Detection-Based Methods 94 5 Detection of Moving Violations 101 Wencheng Wu, Orhan Bulan, Edgar A. Bernal and Robert P. Loce 5.1 Introduction 101 5.2 Detection of Speed Violations 101 5.2.1 Speed Estimation from Monocular Cameras 102 5.2.2 Speed Estimation from Stereo Cameras 108 5.2.3 Discussion 115 5.3 Stop Violations 115 5.3.1 Red Light Cameras 115 5.4 Other Violations 125 5.4.1 Wrong-Way Driver Detection 125 5.4.2 Crossing Solid Lines 126 6 Traffic Flow Analysis 131 Rodrigo Fernandez, Muhammad Haroon Yousaf, Timothy J. Ellis, Zezhi Chen and Sergio A. Velastin 6.1 What is Traffic Flow Analysis? 131 6.1.1 Traffic Conflicts and Traffic Analysis 131 6.1.2 Time Observation 132 6.1.3 Space Observation 133 6.1.4 The Fundamental Equation 133 6.1.5 The Fundamental Diagram 133 6.1.6 Measuring Traffic Variables 134 6.1.7 Road Counts 135 6.1.8 Junction Counts 135 6.1.9 Passenger Counts 136 6.1.10 Pedestrian Counts 136 6.1.11 Speed Measurement 136 6.2 The Use of Video Analysis in Intelligent Transportation Systems 137 6.2.1 Introduction 137 6.2.2 General Framework for Traffic Flow Analysis 137 6.2.3 Application Domains 143 6.3 Measuring Traffic Flow from Roadside CCTV Video 144 6.3.1 Video Analysis Framework 144 6.3.2 Vehicle Detection 146 6.3.3 Background Model 146 6.3.4 Counting Vehicles 149 6.3.5 Tracking 150 6.3.6 Camera Calibration 150 6.3.7 Feature Extraction and Vehicle Classification 152 6.3.8 Lane Detection 153 6.3.9 Results 155 6.4 Some Challenges 156 7 Intersection Monitoring Using Computer Vision Techniques for Capacity, Delay, and Safety Analysis 163 Brendan Tran Morris and Mohammad Shokrolah Shirazi 7.1 Vision-Based Intersection Analysis: Capacity, Delay, and Safety 163 7.1.1 Intersection Monitoring 163 7.1.2 Computer Vision Application 164 7.2 System Overview 165 7.2.1 Tracking Road Users 166 7.2.2 Camera Calibration 169 7.3 Count Analysis 171 7.3.1 Vehicular Counts 171 7.3.2 Nonvehicular Counts 173 7.4 Queue Length Estimation 173 7.4.1 Detection-Based Methods 174 7.4.2 Tracking-Based Methods 175 7.5 Safety Analysis 177 7.5.1 Behaviors 178 7.5.2 Accidents 182 7.5.3 Conflicts 185 7.6 Challenging Problems and Perspectives 187 7.6.1 Robust Detection and Tracking 187 7.6.2 Validity of Prediction Models for Conflict and Collisions 188 7.6.3 Cooperating Sensing Modalities 189 7.6.4 Networked Traffic Monitoring Systems 189 7.7 Conclusion 189 8 Video-Based Parking Management 195 Oliver Sidla and Yuriy Lipetski 8.1 Introduction 195 8.2 Overview of Parking Sensors 197 8.3 Introduction to Vehicle Occupancy Detection Methods 200 8.4 Monocular Vehicle Detection 200 8.4.1 Advantages of Simple 2D Vehicle Detection 200 8.4.2 Background Model–Based Approaches 200 8.4.3 Vehicle Detection Using Local Feature Descriptors 202 8.4.4 Appearance-Based Vehicle Detection 203 8.4.5 Histograms of Oriented Gradients 204 8.4.6 LBP Features and LBP Histograms 207 8.4.7 Combining Detectors into Cascades and Complex Descriptors 208 8.4.8 Case Study: Parking Space Monitoring Using a Combined Feature Detector 208 8.4.9 Detection Using Artificial Neural Networks 211 8.5 Introduction to Vehicle Detection with 3D Methods 213 8.6 Stereo Vision Methods 215 8.6.1 Introduction to Stereo Methods 215 8.6.2 Limits on the Accuracy of Stereo Reconstruction 216 8.6.3 Computing the Stereo Correspondence 217 8.6.4 Simple Stereo for Volume Occupation Measurement 218 8.6.5 A Practical System for Parking Space Monitoring Using a Stereo System 218 8.6.6 Detection Methods Using Sparse 3D Reconstruction 220 9 Video Anomaly Detection 227 Raja Bala and Vishal Monga 9.1 Introduction 227 9.2 Event Encoding 228 9.2.1 Trajectory Descriptors 229 9.2.2 Spatiotemporal Descriptors 231 9.3 Anomaly Detection Models 233 9.3.1 Classification Methods 233 9.3.2 Hidden Markov Models 234 9.3.3 Contextual Methods 234 9.4 Sparse Representation Methods for Robust Video Anomaly Detection 236 9.4.1 Structured Anomaly Detection 237 9.4.2 Unstructured Video Anomaly Detection 243 9.4.3 Experimental Setup and Results 245 9.5 Conclusion and Future Research 253 Part II Imaging from and within the Vehicle 257 10 Pedestrian Detection 259 Shashank Deshpande and Yang Cai 10.1 Introduction 259 10.2 Overview of the Algorithms 259 10.3 Thermal Imaging 260 10.4 Background Subtraction Methods 261 10.4.1 Frame Subtraction 261 10.4.2 Approximate Median 262 10.4.3 Gaussian Mixture Model 263 10.5 Polar Coordinate Profile 263 10.6 Image-Based Features 265 10.6.1 Histogram of Oriented Gradients 265 10.6.2 Deformable Parts Model 266 10.6.3 LiDAR and Camera Fusion–Based Detection 266 10.7 LiDAR Features 268 10.7.1 Preprocessing Module 268 10.7.2 Feature Extraction Module 268 10.7.3 Fusion Module 268 10.7.4 LIPD Dataset 270 10.7.5 Overview of the Algorithm 270 10.7.6 LiDAR Module 272 10.7.7 Vision Module 275 10.7.8 Results and Discussion 276 10.7.8.1 LiDAR Module 276 10.7.8.2 Vision Module 276 10.8 Summary 280 11 Lane Detection and Tracking Problems in Lane Departure Warning Systems 283 Gianni Cario, Alessandro Casavola and Marco Lupia 11.1 Introduction 283 11.2 LD: Algorithms for a Single Frame 285 11.2.1 Image Preprocessing 285 11.2.2 Edge Extraction 287 11.2.3 Stripe Identification 291 11.2.4 Line Fitting 294 11.3 LT Algorithms 297 11.3.1 Recursive Filters on Subsequent N frames 298 11.3.2 Kalman Filter 298 11.4 Implementation of an LD and LT Algorithm 299 11.4.1 Simulations 300 11.4.2 Test Driving Scenario 300 11.4.3 Driving Scenario: Lane Departures at Increasing Longitudinal Speed 300 11.4.4 The Proposed Algorithm 302 11.4.5 Conclusions 303 12 Vision-Based Integrated Techniques for Collision Avoidance Systems 305 Ravi Satzoda and Mohan Trivedi 12.1 Introduction 305 12.2 Related Work 307 12.3 Context Definition for Integrated Approach 307 12.4 ELVIS: Proposed Integrated Approach 308 12.4.1 Vehicle Detection Using Lane Information 309 12.4.2 Improving Lane Detection using On-Road Vehicle Information 312 12.5 Performance Evaluation 313 12.5.1 Vehicle Detection in ELVIS 313 12.5.2 Lane Detection in ELVIS 316 12.6 Concluding Remarks 319 13 Driver Monitoring 321 Raja Bala and Edgar A. Bernal 13.1 Introduction 321 13.2 Video Acquisition 322 13.3 Face Detection and Alignment 323 13.4 Eye Detection and Analysis 325 13.5 Head Pose and Gaze Estimation 326 13.5.1 Head Pose Estimation 326 13.5.2 Gaze Estimation 328 13.6 Facial Expression Analysis 332 13.7 Multimodal Sensing and Fusion 334 13.8 Conclusions and Future Directions 336 14 Traffic Sign Detection and Recognition 343 Hasan Fleyeh 14.1 Introduction 343 14.2 Traffic Signs 344 14.2.1 The European Road and Traffic Signs 344 14.2.2 The American Road and Traffic Signs 347 14.3 Traffic Sign Recognition 347 14.4 Traffic Sign Recognition Applications 348 14.5 Potential Challenges 349 14.6 Traffic Sign Recognition System Design 349 14.6.1 Traffic Signs Datasets 352 14.6.2 Colour Segmentation 354 14.6.3 Traffic Sign's Rim Analysis 359 14.6.4 Pictogram Extraction 364 14.6.5 Pictogram Classification Using Features 365 14.7 Working Systems 369 15 Road Condition Monitoring 375 Matti Kutila, Pasi Pyykonen, Johan Casselgren and Patrik Jonsson 15.1 Introduction 375 15.2 Measurement Principles 376 15.3 Sensor Solutions 377 15.3.1 Camera-Based Friction Estimation Systems 377 15.3.2 Pavement Sensors 379 15.3.3 Spectroscopy 380 15.3.4 Roadside Fog Sensing 382 15.3.5 In-Vehicle Sensors 383 15.4 Classification and Sensor Fusion 386 15.5 Field Studies 390 15.6 Cooperative Road Weather Services 394 15.7 Discussion and Future Work 395 Index 399

Read more less

Book SynopsisProvides comprehensive coverage of theory and hands-on implementation of computer vision-based sensors for structural health monitoring This book is the first to fill the gap between scientific research of computer vision and its practical applications for structural health monitoring (SHM). It provides a complete, state-of-the-art review of the collective experience that the SHM community has gained in recent years. It also extensively explores the potentials of the vision sensor as a fast and cost-effective tool for solving SHM problems based on both time and frequency domain analytics, broadening the application of emerging computer vision sensor technology in not only scientific research but also engineering practice. Computer Vision for Structural Dynamics and Health Monitoring presents fundamental knowledge, important issues, and practical techniques critical to successful development of vision-based sensors in detail, including robustness of template matching techniques for tTable of ContentsList of Figures ix List of Tables xv Series Preface xvii Preface xix About the Companion Website xxi 1 Introduction 1 1.1 Structural Health Monitoring: A Quick Review 1 1.2 Computer Vision Sensors for Structural Health Monitoring 3 1.3 Organization of the Book 7 2 Development of a Computer Vision Sensor for Structural Displacement Measurement 11 2.1 Vision Sensor System Hardware 11 2.2 Vision Sensor System Software: Template-Matching Techniques 15 2.2.1 Area-Based Template Matching 16 2.2.2 Feature-Based Template Matching 20 2.3 Coordinate Conversion and Scaling Factors 22 2.3.1 Camera Calibration Method 23 2.3.2 Practical Calibration Method 25 2.4 Representative Template Matching Algorithms 28 2.4.1 Intensity-Based UCC Technique 28 2.4.2 Gradient-Based Robust OCM Technique 33 2.4.3 Vision Sensor Software Package and Operation 39 2.5 Summary 40 3 Performance Evaluation Through Laboratory and Field Tests 43 3.1 Seismic Shaking Table Test 43 3.2 Shaking Table Test of Frame Structure 1 46 3.2.1 Test Description 46 3.2.2 Subpixel Resolution 47 3.2.3 Performance When Tracking Artificial Targets 48 3.2.4 Performance When Tracking Natural Targets 49 3.2.5 Error Quantification 51 3.2.6 Evaluation of OCM and UCC Robustness 51 3.3 Seismic Shaking Table Test of Frame Structure 2 56 3.4 Free Vibration Test of a Beam Structure 59 3.4.1 Test Description 59 3.4.2 Evaluation of the Practical Calibration Method 60 3.5 Field Test of a Pedestrian Bridge 63 3.6 Field Test of a Highway Bridge 66 3.7 Field Test of Two Railway Bridges 67 3.7.1 Test Description 69 3.7.2 Daytime Measurements 72 3.7.3 Nighttime Measurements 72 3.7.4 Field Performance Evaluation 75 3.8 Remote Measurement of the Vincent Thomas Bridge 81 3.9 Remote Measurement of the Manhattan Bridge 82 3.10 Summary 87 4 Application in Modal Analysis, Model Updating, and Damage Detection 89 4.1 Experimental Modal Analysis 91 4.1.1 Modal Analysis of a Frame 91 4.1.2 Modal Analysis of a Beam 97 4.2 Model Updating as a Frequency-Domain Optimization Problem 101 4.3 Damage Detection 108 4.3.1 Mode Shape Curvature-Based Damage Index 108 4.3.2 Test Description 109 4.3.3 Damage Detection Results 110 4.4 Summary 112 5 Application in Model Updating of Railway Bridges under Trainloads 115 5.1 Field Measurement of Bridge Displacement under Trainloads 116 5.2 Formulation of the Finite Element Model 118 5.2.1 Modeling the Train-Track-Bridge Interaction 118 5.2.2 Finite Element Model of the Railway Bridge 120 5.3 Sensitivity Analysis and Finite Element Model Updating 121 5.3.1 Model Updating as a Time-Domain Optimization Problem 122 5.3.2 Sensitivity Analysis of Displacement and Acceleration Responses 123 5.3.3 Finite Element Model Updating 127 5.4 Dynamic Characteristics of Short-Span Bridges under Trainloads 130 5.5 Summary 136 6 Application in Simultaneously Identifying Structural Parameters and Excitation Forces 139 6.1 Simultaneous Identification Using Vision-Based Displacement Measurements 140 6.1.1 Structural Parameter Identification as a Time-Domain Optimization Problem 141 6.1.2 Force Identification Based on Structural Displacement Measurements 142 6.1.3 Simultaneous Identification Procedure 144 6.2 Numerical Example 146 6.2.1 Robustness to Noise and Number of Sensors 147 6.2.2 Robustness to Initial Stiffness Values 150 6.2.3 Robustness to Damping Ratio Values 150 6.3 Experimental Validation 154 6.3.1 Test Description 154 6.3.2 Identification Results 155 6.4 Summary 157 7 Application in Estimating Cable Force 171 7.1 Vision Sensor for Estimating Cable Force 172 7.1.1 Vibration Method 172 7.1.2 Procedure for Vision-Based Cable Tension Estimation 173 7.2 Implementation in the Hard Rock Stadium Renovation Project 174 7.2.1 Hard Rock Stadium 175 7.2.2 Test Description 176 7.2.3 Estimating and Validating Cable Force 178 7.3 Implementation in the Bronx-Whitestone Bridge Suspender Replacement Project 184 7.3.1 Bronx-Whitestone Bridge 184 7.3.2 Estimating Suspender Tension 185 7.4 Summary 187 8 Achievements, Challenges, and Opportunities 191 8.1 Capabilities of Vision-Based Displacement Sensors: A Summary 191 8.1.1 Artificial vs. Natural Targets 192 8.1.2 Single-Point vs. Multipoint Measurements 192 8.1.3 Pixel vs. Subpixel Resolution 193 8.1.4 2D vs. 3D Measurements 194 8.1.5 Real Time vs. Post Processing 194 8.2 Sources of Error in Vision-Based Displacement Sensors 195 8.2.1 Camera Motion 196 8.2.2 Coordinate Conversion 197 8.2.3 Hardware Limitations 198 8.2.4 Environmental Sources 198 8.3 Vision-Based Displacement Sensors for Structural Health Monitoring 199 8.3.1 Dynamic Displacement Measurement 199 8.3.2 Modal Property Identification 201 8.3.3 Model Updating and Damage Detection 202 8.3.4 Cable Force Estimation 203 8.4 Other Civil and Structural Engineering Applications 204 8.4.1 Automated Machine Visual Inspection 204 8.4.2 Onsite Construction Tracking and Safety Monitoring 206 8.4.3 Vehicle Load Estimation 206 8.4.4 Other Applications 207 8.5 Future Research Directions 208 Appendix: Fundamentals of Digital Image Processing Using MATLAB 211 A.1 Digital Image Representation 211 A.2 Noise Removal 214 A.3 Edge Detection 216 A.4 Discrete Fourier Transform 217 References 221 Index 229

Read more less

Book SynopsisOBJECT DETECTION BY STEREO VISION IMAGES Since both theoretical and practical aspects of the developments in this field of research are explored, including recent state-of-the-art technologies and research opportunities in the area of object detection, this book will act as a good reference for practitioners, students, and researchers. Current state-of-the-art technologies have opened up new opportunities in research in the areas of object detection and recognition of digital images and videos, robotics, neural networks, machine learning, stereo vision matching algorithms, soft computing, customer prediction, social media analysis, recommendation systems, and stereo vision. This book has been designed to provide directions for those interested in researching and developing intelligent applications to detect an object and estimate depth. In addition to focusing on the performance of the system using high-performance computing techniques, a technical overview of certain tools, languages,Table of ContentsPreface xiii 1 Data Conditioning for Medical Imaging 1 Shahzia Sayyad, Deepti Nikumbh, Dhruvi Lalit Jain, Prachi Dhiren Khatri, Alok Saratchandra Panda and Rupesh Ravindra Joshi 1.1 Introduction 2 1.2 Importance of Image Preprocessing 2 1.3 Introduction to Digital Medical Imaging 3 1.3.1 Types of Medical Images for Screening 4 1.3.1.1 X-rays 4 1.3.1.2 Computed Tomography (CT) Scan 4 1.3.1.3 Ultrasound 4 1.3.1.4 Magnetic Resonance Imaging (MRI) 5 1.3.1.5 Positron Emission Tomography (PET) Scan 5 1.3.1.6 Mammogram 5 1.3.1.7 Fluoroscopy 5 1.3.1.8 Infrared Thermography 6 1.4 Preprocessing Techniques of Medical Imaging Using Python 6 1.4.1 Medical Image Preprocessing 6 1.4.1.1 Reading the Image 7 1.4.1.2 Resizing the Image 7 1.4.1.3 Noise Removal 8 1.4.1.4 Filtering and Smoothing 9 1.4.1.5 Image Segmentation 11 1.5 Medical Image Processing Using Python 13 1.5.1 Medical Image Processing Methods 16 1.5.1.1 Image Formation 17 1.5.1.2 Image Enhancement 19 1.5.1.3 Image Analysis 19 1.5.1.4 Image Visualization 19 1.5.1.5 Image Management 19 1.6 Feature Extraction Using Python 20 1.7 Case Study on Throat Cancer 24 1.7.1 Introduction 24 1.7.1.1 HSI System 25 1.7.1.2 The Adaptive Deep Learning Method Proposed 25 1.7.2 Results and Findings 27 1.7.3 Discussion 28 1.7.4 Conclusion 29 1.8 Conclusion 29 References 30 Additional Reading 31 Key Terms and Definition 32 2 Detection of Pneumonia Using Machine Learning and Deep Learning Techniques: An Analytical Study 33 Shravani Nimbolkar, Anuradha Thakare, Subhradeep Mitra, Omkar Biranje and Anant Sutar 2.1 Introduction 33 2.2 Literature Review 35 2.3 Learning Methods 41 2.3.1 Machine Learning 41 2.3.2 Deep Learning 42 2.3.3 Transfer Learning 42 2.4 Detection of Lung Diseases Using Machine Learning and Deep Learning Techniques 43 2.4.1 Dataset Description 43 2.4.2 Evaluation Platform 44 2.4.3 Training Process 44 2.4.4 Model Evaluation of CNN Classifier 46 2.4.5 Mathematical Model 47 2.4.6 Parameter Optimization 47 2.4.7 Performance Metrics 50 2.5 Conclusion 52 References 53 3 Contamination Monitoring System Using IOT and GIS 57 Kavita R. Singh, Ravi Wasalwar, Ajit Dharmik and Deepshikha Tiwari 3.1 Introduction 58 3.2 Literature Survey 58 3.3 Proposed Work 60 3.4 Experimentation and Results 61 3.4.1 Experimental Setup 61 3.5 Results 64 3.6 Conclusion 70 Acknowledgement 71 References 71 4 Video Error Concealment Using Particle Swarm Optimization 73 Rajani P. K. and Arti Khaparde 4.1 Introduction 74 4.2 Proposed Research Work Overview 75 4.3 Error Detection 75 4.4 Frame Replacement Video Error Concealment Algorithm 77 4.5 Research Methodology 77 4.5.1 Particle Swarm Optimization 78 4.5.2 Spatio-Temporal Video Error Concealment Method 78 4.5.3 Proposed Modified Particle Swarm Optimization Algorithm 79 4.6 Results and Analysis 83 4.6.1 Single Frame With Block Error Analysis 85 4.6.2 Single Frame With Random Error Analysis 86 4.6.3 Multiple Frame Error Analysis 88 4.6.4 Sequential Frame Error Analysis 91 4.6.5 Subjective Video Quality Analysis for Color Videos 93 4.6.6 Scene Change of Videos 94 4.7 Conclusion 95 4.8 Future Scope 97 References 97 5 Enhanced Image Fusion with Guided Filters 99 Nalini Jagtap and Sudeep D. Thepade 5.1 Introduction 100 5.2 Related Works 100 5.3 Proposed Methodology 102 5.3.1 System Model 102 5.3.2 Steps of the Proposed Methodology 104 5.4 Experimental Results 104 5.4.1 Entropy 104 5.4.2 Peak Signal-to-Noise Ratio 105 5.4.3 Root Mean Square Error 107 5.4.3.1 Qab/f 108 5.5 Conclusion 108 References 109 6 Deepfake Detection Using LSTM-Based Neural Network 111 Tejaswini Yesugade, Shrikant Kokate, Sarjana Patil, Ritik Varma and Sejal Pawar 6.1 Introduction 111 6.2 Related Work 112 6.2.1 Deepfake Generation 112 6.2.2 LSTM and CNN 112 6.3 Existing System 113 6.3.1 AI-Generated Fake Face Videos by Detecting Eye Blinking 113 6.3.2 Detection Using Inconsistence in Head Pose 113 6.3.3 Exploiting Visual Artifacts 113 6.4 Proposed System 114 6.4.1 Dataset 114 6.4.2 Preprocessing 114 6.4.3 Model 115 6.5 Results 117 6.6 Limitations 119 6.7 Application 119 6.8 Conclusion 119 References 119 7 Classification of Fetal Brain Abnormalities with MRI Images: A Survey 121 Kavita Shinde and Anuradha Thakare 7.1 Introduction 121 7.2 Related Work 123 7.3 Evaluation of Related Research 129 7.4 General Framework for Fetal Brain Abnormality Classification 129 7.4.1 Image Acquisition 130 7.4.2 Image Pre-Processing 130 7.4.2.1 Image Thresholding 130 7.4.2.2 Morphological Operations 131 7.4.2.3 Hole Filling and Mask Generation 131 7.4.2.4 MRI Segmentation for Fetal Brain Extraction 132 7.4.3 Feature Extraction 132 7.4.3.1 Gray-Level Co-Occurrence Matrix 133 7.4.3.2 Discrete Wavelet Transformation 133 7.4.3.3 Gabor Filters 134 7.4.3.4 Discrete Statistical Descriptive Features 134 7.4.4 Feature Reduction 134 7.4.4.1 Principal Component Analysis 135 7.4.4.2 Linear Discriminant Analysis 136 7.4.4.3 Non-Linear Dimensionality Reduction Techniques 137 7.4.5 Classification by Using Machine Learning Classifiers 137 7.4.5.1 Support Vector Machine 138 7.4.5.2 K-Nearest Neighbors 138 7.4.5.3 Random Forest 139 7.4.5.4 Linear Discriminant Analysis 139 7.4.5.5 Naïve Bayes 139 7.4.5.6 Decision Tree (DT) 140 7.4.5.7 Convolutional Neural Network 140 7.5 Performance Metrics for Research in Fetal Brain Analysis 141 7.6 Challenges 142 7.7 Conclusion and Future Works 142 References 143 8 Analysis of COVID-19 Data Using Machine Learning Algorithm 147 Chinnaiah Kotadi, Mithun Chakravarthi K., Srihari Chintha and Kapil Gupta 8.1 Introduction 147 8.2 Pre-Processing 148 8.3 Selecting Features 149 8.4 Analysis of COVID-19–Confirmed Cases in India 152 8.4.1 Analysis to Highest COVID-19–Confirmed Case States in India 153 8.4.2 Analysis to Highest COVID-19 Death Rate States in India 153 8.4.3 Analysis to Highest COVID-19 Cured Case States in India 154 8.4.4 Analysis of Daily COVID-19 Cases in Maharashtra State 155 8.5 Linear Regression Used for Predicting Daily Wise COVID- 19 Cases in Maharashtra 156 8.6 Conclusion 157 References 157 9 Intelligent Recommendation System to Evaluate Teaching Faculty Performance Using Adaptive Collaborative Filtering 159 Manish Sharma and Rutuja Deshmukh 9.1 Introduction 160 9.2 Related Work 162 9.3 Recommender Systems and Collaborative Filtering 164 9.4 Proposed Methodology 165 9.5 Experiment Analysis 167 9.6 Conclusion 168 References 168 10 Virtual Moratorium System 171 Manisha Bhende, Muzasarali Badger, Pranish Kumbhar, Vedanti Bhatkar and Payal Chavan 10.1 Introduction 172 10.1.1 Objectives 172 10.2 Literature Survey 172 10.2.1 Virtual Assistant—BLU 172 10.2.2 HDFC Ask EVA 173 10.3 Methodologies of Problem Solving 173 10.4 Modules 174 10.4.1 Chatbot 174 10.4.2 Android Application 175 10.4.3 Web Application 175 10.5 Detailed Flow of Proposed Work 176 10.5.1 System Architecture 176 10.5.2 DFD Level 1 177 10.6 Architecture Design 178 10.6.1 Main Server 178 10.6.2 Chatbot 178 10.6.3 Database Architecture 180 10.6.4 Web Scraper 180 10.7 Algorithms Used 181 10.7.1 AES-256 Algorithm 181 10.7.2 Rasa NLU 181 10.8 Results 182 10.9 Discussions 183 10.9.1 Applications 183 10.9.2 Future Work 183 10.9.3 Conclusion 183 References 183 11 Efficient Land Cover Classification for Urban Planning 185 Vandana Tulshidas Chavan and Sanjeev J. Wagh 11.1 Introduction 185 11.2 Literature Survey 189 11.3 Proposed Methodology 191 11.4 Conclusion 192 References 192 12 Data-Driven Approches for Fake News Detection on Social Media Platforms: Review 195 Pradnya Patil and Sanjeev J. Wagh 12.1 Introduction 196 12.2 Literature Survey 196 12.3 Problem Statement and Objectives 201 12.3.1 Problem Statement 201 12.3.2 Objectives 201 12.4 Proposed Methodology 202 12.4.1 Pre-Processing 202 12.4.2 Feature Extraction 203 12.4.3 Classification 203 12.5 Conclusion 204 References 204 13 Distance Measurement for Object Detection for Automotive Applications Using 3D Density-Based Clustering 207 Anupama Patil, Manisha Bhende, Suvarna Patil and P. P. Shevatekar 13.1 Introduction 208 13.2 Related Work 210 13.3 Distance Measurement Using Stereo Vision 213 13.3.1 Calibration of the Camera 215 13.3.2 Stereo Image Rectification 215 13.3.3 Disparity Estimation and Stereo Matching 216 13.3.4 Measurement of Distance 217 13.4 Object Segmentation in Depth Map 218 13.4.1 Formation of Depth Map 218 13.4.2 Density-Based in 3D Object Grouping Clustering 218 13.4.3 Layered Images Object Segmentation 219 13.4.3.1 Image Layer Formation 221 13.4.3.2 Determination of Object Boundaries 222 13.5 Conclusion 223 References 224 14 Real-Time Depth Estimation Using BLOB Detection/ Contour Detection 227 Arokia Priya Charles, Anupama V. Patil and Sunil Dambhare 14.1 Introduction 227 14.2 Estimation of Depth Using Blob Detection 229 14.2.1 Grayscale Conversion 230 14.2.2 Thresholding 231 14.2.3 Image Subtraction in Case of Input with Background 232 14.2.3.1 Preliminaries 233 14.2.3.2 Computing Time 234 14.3 Blob 234 14.3.1 BLOB Extraction 234 14.3.2 Blob Classification 235 14.3.2.1 Image Moments 236 14.3.2.2 Centroid Using Image Moments 238 14.3.2.3 Central Moments 238 14.4 Challenges 241 14.5 Experimental Results 241 14.6 Conclusion 251 References 255 Index 257

Read more less

Book SynopsisImage Segmentation Summarizes and improves new theory, methods, and applications of current image segmentation approaches, written by leaders in the field The process of image segmentation divides an image into different regions based on the characteristics of pixels, resulting in a simplified image that can be more efficiently analyzed. Image segmentation has wide applications in numerous fields ranging from industry detection and bio-medicine to intelligent transportation and architecture. Image Segmentation: Principles, Techniques, and Applications is an up-to-date collection of recent techniques and methods devoted to the field of computer vision. Covering fundamental concepts, new theories and approaches, and a variety of practical applications including medical imaging, remote sensing, fuzzy clustering, and watershed transform. In-depth chapters present innovative methods developed by the authorssuch as convolutional neural networks, graph convolutional networks, deformable convolution, and model compressionto assist graduate students and researchers apply and improve image segmentation in their work. Describes basic principles of image segmentation and related mathematical methods such as clustering, neural networks, and mathematical morphology. Introduces new methods for achieving rapid and accurate image segmentation based on classic image processing and machine learning theory. Presents techniques for improved convolutional neural networks for scene segmentation, object recognition, and change detection, etc. Highlights the effect of image segmentation in various application scenarios such as traffic image analysis, medical image analysis, remote sensing applications, and material analysis, etc. Image Segmentation: Principles, Techniques, and Applications is an essential resource for undergraduate and graduate courses such as image and video processing, computer vision, and digital signal processing, as well as researchers working in computer vision and image analysis looking to improve their techniques and methods.Table of ContentsPreface About the Authors List of Abbreviations Part One: Principle 1 Introduction to Image Segmentation 2 Principles of Clustering 3 Principles of Mathematical Morphology 4 Principles of Neural Network Part Two: Methods 5 Fast and Robust Image Segmentation Using Clustering 6 Fast Image Segmentation Using Watershed Transform 7 Superpixel-based Fast Image Segmentation Part Three: Application 8 Image Segmentation for Traffic Scene Analysis 9 Image Segmentation for Medical Analysis 10 Image Segmentation for Remote Sensing Analysis 11 Image Segmentation for Material Analysis

Read more less

Book SynopsisMachine Learning Applications Practical resource on the importance of Machine Learning and Deep Learning applications in various technologies and real-world situations Machine Learning Applications discusses methodological advancements of machine learning and deep learning, presents applications in image processing, including face and vehicle detection, image classification, object detection, image segmentation, and delivers real-world applications in healthcare to identify diseases and diagnosis, such as creating smart health records and medical imaging diagnosis, and provides real-world examples, case studies, use cases, and techniques to enable the reader's active learning. Composed of 13 chapters, this book also introduces real-world applications of machine and deep learning in blockchain technology, cyber security, and climate change. An explanation of AI and robotic applications in mechanical design is also discussed, including robot-assisted surgeries, security, and space explor

Read more less

Book SynopsisComputer Vision Metrics provides an extensive survey and analysis of over 100 current and historical feature description and machine vision methods, with a detailed taxonomy for local, regional and global features.Table of ContentsChapter 1. Image Capture and RepresentationChapter 2. Image Pre-ProcessingChapter 3. Global and Regional FeaturesChapter 4. Local Feature Design Concepts, Classification, and LearningChapter 5. Taxonomy Of Feature Description AttributesChapter 6. Interest Point Detector and Feature Descriptor SurveyChapter 7. Ground Truth Data, Data, Metrics, and AnalysisChapter 8. Vision Pipelines and OptimizationsAppendix A. Synthetic Feature AnalysisAppendix B. Survey of Ground Truth DatasetsAppendix C. Imaging and Computer Vision ResourcesAppendix D. Extended SDM Metrics

Read more less

Book Synopsis1. One Instruction Set Computing.- 1.1 What is One Instruction Set Computing?.- 1.2 Why Study OISC?.- 1.3 A Look Ahead.- 1.4 Exercises.- 2 Instruction Sets.- 2.1 Elements of an Instruction.- 2.2 Operands.- 2.3 Instruction Formats.- 2.4 Core Set of Instructions.- 2.5 Addressing Modes.- 2.6 Exercises.- 3 Types of Computer Architectures.- 3.1 Overview.- 3.2 A Simple Taxonomy.- 3.3 Accumulator.- 3.4 Register-Memory.- 3.5 Register-Oriented.- 3.6 Exercises.- 4 Evolution of Instruction Sets.- 4.1 Motivation.- 4.2 Evolution of Microprocessors.- 4.3 Timeline.- 4.4 Exercises.- 5 CISC, RISC, OISC.- 5.1 CISC versus RISC.- 5.2 Is OISC a CISC or RISC?.- 5.3 Processor Complexity.- 5.4 Exercises.- 6 OISC Architectures.- 6.1 Single Instruction Types.- 6.2 MOVE.- 6.3 Comparing OISC Models.- 6.4 Variants of SBN and MOVE.- 6.5 OISC Continuum.- 6.6 Exercises.- 7 Historical Review of OISC.- 7.1 Subtract and Branch if Negative (SBN).- 7.2 MOVE-based.- 7.3 Timeline.- 7.4 Exercises.- 8 Instruction Set Completeness.- 8.1 Instruction Set Completeness.- 8.2 A Practical Approach to Determining Completeness.- 8.3 Completeness of Two OISCs.- 8.4 Exercises.- 9 OISC Mappings.- 9.1 Mapping OISC to Conventional Architectures.- 9.2 Synthesizing Instructions.- 9.3 Code Fragments.- 9.4 Implementing OISC using OISC.- 9.5 Exercises.- 10 Parallel Architectures.- 10.1 Von Neumann Bottleneck.- 10.2 Parallel Processing.- 10.3 Flynn's Taxonomy for Parallelism.- 10.4 Exercises.- 11 Applications and Implementations.- 11.1 OlSC-like Phenomena.- 11.2 Field Programmable Gate Arrays.- 11.3 Applications.- 11.4 Image Processing.- 11.5 Future Work with OISC.- 11.6 Exercises.- Appendix A: A Generic Microprocessor and OISC.- Appendix B: One Instruction Set Computer Implementation.- B.1 6502 Opcodes Summary.- B.2 6502Opcodes Mapped to MOVE OISC.- B.3 6502 Addressing as MOVE-based OISC.- B.4 6502 Addressing Modes and MOVE-based OISC.- Appendix C: Dilation Code Implementation.- Appendix D: Compiler Output for Dilation.- Appendix E: OISC Equivalent of Dilation.- References.- About the Authors.Trade Review`This book gives a fine introduction to basic computer architecture. A few years ago, this book would have interested only graduate computer science and engineering students. These days, some high school students even create Linux clusters, and interest in it may be even more widespread.' R.P. Sarna, Maine Maritime Academy in Choice, December 2003Table of ContentsPreface. Acknowledgements. - 1: One Instruction Set Computing. 1.1. What is One Instruction Set Computing? 1.2. Why Study OISC? 1.3. A Look Ahead. 1.4. Exercises. 2: Instruction Sets. 2.1. Elements of an Instruction. 2.2. Operands. 2.3. Instruction Formats. 2.4. Core Set of Instructions. 2.5. Addressing Modes. 2.6. Exercises. - 3: Types of Computer Architecture. 3.1. Overview. 3.2.A Simple Taxonomy. 3.3. Accumulator. 3.4. Register-Memory. 3.5. Register-Oriented. 3.6. Exercises. - 4: Evolution of Instruction Sets. 4.1. Motivation. 4.2. Evolution of Microprocessors. 4.3. Timeline. 4.4. Exercises. - 5: CISC, RISC, OISC. 5.1. CISC versus RISC. 5.2. Is OISC a CISC or a RISC? 5.3. Processor Complexity. 5.4. Exercises. - 6: OISC Architectures. 6.1. Single Instruction Types. 6.2. MOVE. 6.3. Comparing OISC Models. 6.4. Variants of SBN and MOVE. 6.5. OISC Continuum. 6.6. Exercises. - 7: Historical Review of OISC. 7.1. Subtract and Branch if Negative (SBN). 7.2. MOVE-Based. 7.3. Timeline. 7.4. Exercises. - 8: Instruction Set Completeness. 8.1. Instruction Set Completeness. 8.2. A Practical Approach to Determining Completeness. 8.3. Completeness of Two OISCs. 8.4. Exercises. - 9: OISC Mappings. 9.1. Mapping OISC to Conventional Architectures. 9.2. Synthesizing Instructions. 9.3. Code Fragments. 9.4. Implementing OISC Using OISC. 9.5. Exercises. - 10: Parallel Architectures. 10.1. Von Neumann Bottleneck. 10.2. Parallel Processing. 10.3. Flynn's Taxonomy for Parallelism. 10.4. Exercises. - 11: Applications and Implementations. 11.1. OISC-Like Phenomena. 11.2. Field Programmable Gate Arrays. 11.3. Applications. 11.4. Image Processing. 11.5. Future Work with OISC. 11.6. Exercises. - Appendix A: A Generic Microprocessor and OISC. - Appendix B: One Instruction Set Computer Implementation. - Appendix C: Dilation Code Implementation. - Appendix D: Compiler Output for Dilation. - Appendix E: OISC Equivalent of Dilation. Glossary. References. Index. About the Authors.

Read more less

Book Synopsisto Computer Algebra.- Algebra of Polynomials, Rational Functions, and Power Series.- Normal Forms and Algebraic Representations.- Arithmetic of Polynomials, Rational Functions, and Power Series.- Homomorphisms and Chinese Remainder Algorithms.- Newton's Iteration and the Hensel Construction.- Polynomial GCD Computation.- Polynomial Factorization.- Solving Systems of Equations.- Gröbner Bases for Polynomial Ideals.- Integration of Rational Functions.- The Risch Integration Algorithm.Trade Review`The Computer Algebra community has been waiting for years for this book to appear. ...the book is a masterpiece and can be recommended to everyone interested in the algorithms for computer algebra, either as a reference for further research or just to give the casual user an idea why things work as good (or as bad) as they do in computer algebra packages. ...the recommendation would be clear: Buy this book! As it stands now my only advice is to stay away from this book, because you might be tempted to buy it anyway (at least I was).' Computer Algebra Nederland Newsletter, June 1993 Table of ContentsPreface. 1. Introduction to Computer Algebra. 2. Algebra of Polynomials, Rational Functions, and Power Series. 3. Normal Forms and Algebraic Representations. 4. Arithmetic of Polynomial, Rational Functions, and Power Series. 5. Homomorphisms and Chinese Remainder Algorithms. 6. Newton's Iteration and the Hensel Construction. 7. Polynomials GCD Computation. 8. Polynomial Factorization. 9. Solving Systems of Equation. 10. Grobner Bases for Polynomial Ideals. 11. Integration of Rational Functions. 12. The Risch Integration Algorithm. Notation. Index.

Read more less

Book SynopsisWhile the deployment of large-scale biometric systems in both commercial and government applications has increased the public awareness of this technology, "Introduction to Biometrics" is the first textbook to introduce the fundamentals of Biometrics to undergraduate/graduate students.Table of ContentsIntroduction.- Fingerprint Recognition.- Face Recognition.- Iris Recognition.- Additional Biometric Traits.- Multibiometrics.- Security of Biometric Systems.

Read more less

Book SynopsisThis book focuses on research trends in image processing and recognition and corresponding developments. Among them, the book focuses on recent research, especially in the field of advanced human-computer interaction and intelligent computing. Given the existing interaction and recognition of the station, some novel topics are proposed, including how to establish a cognitive model in human-computer interaction and how to express and transfer human knowledge into human-machine image recognition. In an interactive implementation, how to implement user experience through image recognition during machine interaction. The main contents of this book are arranged as follows. Chapter 1 introduces the research background, research questions, goals, research questions and overviews of this book. Chapter 2 focuses on image calculation methods based on principal component analysis (PCA) and related extensions. Chapter 3 presents an image processing scheme that takes into account the user experience and the optimal balance between QoE and QoS management. Chapter 4 focuses on the performance analysis of methods for classifying image textures based on local binary patterns. Chapter 5 introduces the generation of the anti-network (GAN) and its methods. Chapter 6 mainly discusses the recognition of the interest target as the visual consciousness of the image computing system and proposes a fuzzy target-based interest target differentiation system, which is applied to the extinction enhancement as a display. Chapter 7 focuses on the implementation and application of PCA image processing and its application in computer vision in the fields of image compression, visual tracking, image recognition, and super-resolution image reconstruction. Chapter 8 introduces various applications of feature extraction and classification techniques in seizures. Chapter 9 introduces some typical image processing based on GAN, involving multiple fields. Chapter 10 introduces an agent-based collaborative information processing framework with stereo vision applications. Chapter 11 introduces the MR application system as a synthesis of the methods and algorithms in each of the above chapters and discusses system design and implementation in terms of functions, modules, and workflows. Chapter 12 evaluates the book, draws conclusions, and proposes advances in image recognition and its advances in image recognition, limitations, and future work, and applies them to intelligent HCI in system design. Objects, human knowledge and user experience, QoE-QoS management, system management, and confidentiality and security.Table of ContentsPreface; Visual Computing for Intelligent Human-Computer Interactions: Trend, Challenges and Progress; Principle Component Analysis Based Computing in Image Recognition; Recognition and Awareness Modeling for Quality of Experience and Quality of Services; Performance Analysis of Local Binary Patterns for Image Texture Classification Methods; Generative Adversarial Networks An Introduction; Knowledge Based Adaptive Fuzzy Strategy for Target of Interest Differentiation; PCA-Based Image Recognition Applications on Vision Based Computing; Review of Feature Extraction and Classification Techniques for Epileptic Seizure Detection; Generative Adversarial Networks Application Domains; Vision-Based Intelligent Human-Computer Interactions with Multiple Agent Collaboration; A Comprehensive Vision-Aware-Computing-Based Interactive System with Agent-Based Collaborative Information Processing; Summary and Feature Works; Index.

Read more less

Book Synopsis

Read more less

Book Synopsis

Read more less

Book Synopsis

Read more less

Book SynopsisDrawing is the starting point for many kinds of tasks, for everyone from children making pictures to professional architects sketching ideas. Drawing seems to be fundamentally connected to how we represent the world visually. Most computer graphics focuses on realistic visual simulation, but over the past few decades, line drawing algorithms have matured, providing the ability to automatically create reasonable line drawings from 3D geometry. This tutorial provides a detailed guide to the mathematical theory and computer algorithms for line drawing of 3D objects. It focuses on the curves known as contours as they are the most important curves for line drawing of 3D surfaces. The authors describe the different algorithms required to compute and render these curves, before going on to explain boundary curves and surface-surface intersection curves. The tutorial concludes with other topics in 3D non-photorealistic rendering including: other types of curves, stroke rendering, and non-photorealistic shading.Line Drawings from 3D Models: A Tutorial is a concise, yet comprehensive, introduction to an increasingly important topic in computer graphics. The extensive bibliography is invaluable for readers wishing to further their own research in the area.Table of Contents 1. Introduction 2. Image-Space Curves 3. Mesh Contours: Definition and Detection 4. Mesh Curve Visibility 5. Smooth Surfaces as Meshes 6. Fast Hardware-Based Extraction and Visibility 7. Parametric Surfaces: Contours and Visibility 8. Implicit Surfaces: Contours and Visibility 9. Stylized Rendering and Animation 10. Conclusion Acknowledgements References

Read more less

Book SynopsisChange Detection and Image Time Series Analysis 1 presents a wide range of unsupervised methods for temporal evolution analysis through the use of image time series associated with optical and/or synthetic aperture radar acquisition modalities. Chapter 1 introduces two unsupervised approaches to multiple-change detection in bi-temporal multivariate images, with Chapters 2 and 3 addressing change detection in image time series in the context of the statistical analysis of covariance matrices. Chapter 4 focuses on wavelets and convolutional-neural filters for feature extraction and entropy-based anomaly detection, and Chapter 5 deals with a number of metrics such as cross correlation ratios and the Hausdorff distance for variational analysis of the state of snow. Chapter 6 presents a fractional dynamic stochastic field model for spatio temporal forecasting and for monitoring fast-moving meteorological events such as cyclones. Chapter 7 proposes an analysis based on characteristic points for texture modeling, in the context of graph theory, and Chapter 8 focuses on detecting new land cover types by classification-based change detection or feature/pixel based change detection. Chapter 9 focuses on the modeling of classes in the difference image and derives a multiclass model for this difference image in the context of change vector analysis.Table of ContentsContents Preface xi Abdourrahmane M. ATTO, Francesca BOVOLO and Lorenzo BRUZZONE List of Notations Chapter 1 Unsupervised Change Detection in Multitemporal Remote Sensing Images 1 Sicong LIU, Francesca BOVOLO, Lorenzo BRUZZONE, QianDU and Xiaohua TONG 1.1. Introduction 1 1.2. Unsupervised change detection in multispectral images 3 1.2.1.Relatedconcepts 3 1.2.2.Openissuesandchallenges 7 1.2.3. Spectral–spatial unsupervised CD techniques 7 1.3 Unsupervised multiclass change detection approaches based on modelingspectral–spatialinformation 9 1.3.1 Sequential spectral change vector analysis (S 2 CVA) 9 1.3.2. Multiscale morphological compressed change vector analysis 11 1.3.3. Superpixel-level compressed change vector analysis 15 1.4.Datasetdescriptionandexperimentalsetup 18 1.4.1.Datasetdescription 18 1.4.2.Experimentalsetup 22 1.5.Resultsanddiscussion 24 1.5.1.ResultsontheXuzhoudataset 24 1.5.2. Results on the Indonesia tsunami dataset 24 xv 1.6.Conclusion 28 1.7.Acknowledgements 29 1.8.References 29 Chapter 2 Change Detection in Time Series of Polarimetric SAR Images 35 Knut CONRADSEN, Henning SKRIVER, MortonJ.CANTY andAllanA.NIELSEN 2.1. Introduction 35 2.1.1.Theproblem 36 2.1.2 Important concepts illustrated by means of the gamma distribution 39 2.2.Testtheoryandmatrixordering 45 2.2.1. Test for equality of two complex Wishart distributions 45 2.2.2. Test for equality of k-complex Wishart distributions 47 2.2.3. The block diagonal case 49 2.2.4.TheLoewnerorder 52 2.3.Thebasicchangedetectionalgorithm 53 2.4.Applications 55 2.4.1.Visualizingchanges 58 2.4.2.Fieldwisechangedetection 59 2.4.3. Directional changes using the Loewner ordering 62 2.4.4. Software availability 65 2.5.References 70 Chapter 3 An Overview of Covariance-based Change Detection Methodologies in Multivariate SAR Image Time Series 73 Ammar MIAN, Guillaume GINOLHAC, Jean-Philippe OVARLEZ, Arnaud BRELOY and Frédéric PASCAL 3.1. Introduction 73 3.2.Datasetdescription 76 3.3.StatisticalmodelingofSARimages 77 3.3.1.Thedata 77 3.3.2.Gaussianmodel 77 3.3.3.Non-Gaussianmodeling 83 3.4.Dissimilaritymeasures 84 3.4.1.Problemformulation 84 3.4.2. Hypothesis testing statistics 85 3.4.3.Information-theoreticmeasures 87 3.4.4.Riemanniangeometrydistances 89 3.4.5.Optimaltransport 90 3.4.6.Summary 91 3.4.7. Results of change detectors on the UAVSAR dataset 91 3.5. Change detection based on structured covariances 94 3.5.1. Low-rank Gaussian change detector 96 3.5.2. Low-rank compound Gaussian change detector 97 3.5.3. Results of low-rank change detectors on the UAVSAR dataset 100 3.6.Conclusion 102 3.7.References 103 Chapter 4 Unsupervised Functional Information Clustering in Extreme Environments from Filter Banks and Relative Entropy 109 Abdourrahmane M. ATTO, Fatima KARBOU, Sophie GIFFARD-ROISIN and Lionel BOMBRUN 4.1. Introduction 109 4.2.Parametricmodelingofconvnetfeatures 110 4.3.Anomalydetectioninimagetimeseries 113 4.4.Functionalimagetimeseriesclustering 119 4.5.Conclusion 123 4.6.References 123 Chapter 5 Thresholds and Distances to Better Detect Wet Snow over Mountains with Sentinel-1 Image Time Series 127 Fatima KARBOU, Guillaume JAMES, Philippe DURAND and Abdourrahmane M. ATTO 5.1. Introduction 127 5.2.Testareaanddata 129 5.3.WetsnowdetectionusingSentinel-1 129 5.4.Metricstodetectwetsnow 133 5.5.Discussion 138 5.6.Conclusion 143 5.7.Acknowledgements 143 5.8.References 143 Chapter 6 Fractional Field Image Time Series Modeling and Application to Cyclone Tracking 145 Abdourrahmane M. ATTO, Aluísio PINHEIRO, Guillaume GINOLHAC and Pedro MORETTIN 6.1. Introduction 145 6.2. Random field model of a cyclone texture 148 6.2.1.Cyclonetexturefeature 149 6.2.2. Wavelet-based power spectral densities and cyclone fields 150 6.2.3. Fractional spectral power decay model 153 6.3.Cyclonefieldeyedetectionandtracking 157 6.3.1.Cycloneeyedetection 157 6.3.2.Dynamicfractalfieldeyetracking 158 6.4. Cyclone field intensity evolution prediction 159 6.5.Discussion 161 6.6.Acknowledgements 163 6.7.References 163 Chapter 7 Graph of Characteristic Points for Texture Tracking: Application to Change Detection and Glacier Flow Measurement from SAR Images 167 Minh-Tan PHAM and Grégoire MERCIER 7.1. Introduction 167 7.2. Texture representation and characterization using local extrema 169 7.2.1.Motivationandapproach 169 7.2.2. Local extrema keypoints within SAR images 172 7.3.Unsupervisedchangedetection 175 7.3.1. Proposed framework 175 7.3.2. Weighted graph construction from keypoints 176 7.3.3.Changemeasure(CM)generation 178 7.4.Experimentalstudy 179 7.4.1. Data description and evaluation criteria 179 7.4.2.Changedetectionresults 181 7.4.3.Sensitivitytoparameters 185 7.4.4.ComparisonwiththeNLMmodel 188 7.4.5. Analysis of the algorithm complexity 191 7.5.Applicationtoglacierflowmeasurement 192 7.5.1. Proposed method 193 7.5.2.Results 194 7.6.Conclusion 196 7.7.References 197 Chapter 8 Multitemporal Analysis of Sentinel-1/2 Images for Land Use Monitoring at Regional Scale 201 Andrea GARZELLI and Claudia ZOPPETTI 8.1. Introduction 201 8.2. Proposed method 203 8.2.1.Testsiteanddata 206 8.3.SARprocessing 209 8.4.Opticalprocessing 215 8.5.Combinationlayer 217 8.6.Results 219 8.7.Conclusion 220 8.8.References 221 Chapter 9 Statistical Difference Models for Change Detection in Multispectral Images 223 Massimo ZANETTI, Francesca BOVOLO and Lorenzo BRUZZONE 9.1. Introduction 223 9.2. Overview of the change detection problem 225 9.2.1. Change detection methods for multispectral images 227 9.2.2. Challenges addressed in this chapter 230 9.3 The Rayleigh–Rice mixture model for the magnitude of the differenceimage 231 9.3.1. Magnitude image statistical mixture model 231 9.3.2.Bayesiandecision 233 9.3.3. Numerical approach to parameter estimation 234 9.4. A compound multiclass statistical model of the difference image 239 9.4.1. Difference image statistical mixture model 240 9.4.2. Magnitude image statistical mixture model 245 9.4.3.Bayesiandecision 248 9.4.4. Numerical approach to parameter estimation 249 9.5.Experimentalresults 253 9.5.1.Datasetdescription 253 9.5.2.Experimentalsetup 256 9.5.3. Test 1: Two-class Rayleigh–Rice mixture model 256 9.5.4. Test 2: Multiclass Rician mixture model 260 9.6.Conclusion 266 9.7.References 267 List of Authors 275 Index 277 Summary of Volume 2 281

Read more less

Book SynopsisChange Detection and Image Time Series Analysis 2 presents supervised machine-learning-based methods for temporal evolution analysis by using image time series associated with Earth observation data. Chapter 1 addresses the fusion of multisensor, multiresolution and multitemporal data. It proposes two supervised solutions that are based on a Markov random field: the first relies on a quad-tree and the second is specifically designed to deal with multimission, multifrequency and multiresolution time series.Chapter 2 provides an overview of pixel based methods for time series classification, from the earliest shallow learning methods to the most recent deep-learning-based approaches.Chapter 3 focuses on very high spatial resolution data time series and on the use of semantic information for modeling spatio-temporal evolution patterns.Chapter 4 centers on the challenges of dense time series analysis, including pre processing aspects and a taxonomy of existing methodologies. Finally, since the evaluation of a learning system can be subject to multiple considerations,Chapters 5 and 6 offer extensive evaluations of the methodologies and learning frameworks used to produce change maps, in the context of multiclass and/or multilabel change classification issues.Table of ContentsContents Preface ix Abdourrahmane M. ATTO, Francesca BOVOLO and Lorenzo BRUZZONE List of Notations Chapter 1 Hierarchical Markov Random Fields for High Resolution Land Cover Classification of Multisensor and Multiresolution Image Time Series 1 Ihsen HEDHLI, Gabriele MOSER, Sebastiano B. SERPICO and Josiane ZERUBIA 1.1. Introduction 1 1.1.1. The role of multisensor data in time series classification 1 1.1.2. Multisensor and multiresolution classification 2 1.1.3.Previouswork 5 1.2. Methodology 9 1.2.1. Overview of the proposed approaches 9 1.2.2. Hierarchical model associated with the first proposed method 10 1.2.3. Hierarchical model associated with the second proposed method 13 1.2.4. Multisensor hierarchical MPM inference 14 1.2.5. Probability density estimation through finite mixtures 17 1.3.Examplesofexperimentalresults 19 1.3.1.Resultsofthefirstmethod 19 1.3.2.Resultsofthesecondmethod 22 1.4.Conclusion 26 xiii 1.5.Acknowledgments 26 1.6.References 27 Chapter 2 Pixel-based Classification Techniques for Satellite Image Time Series 33 Charlotte PELLETIER and Silvia VALERO 2.1. Introduction 33 2.2. Basic concepts in supervised remote sensing classification 35 2.2.1. Preparing data before it is fed into classification algorithms 35 2.2.2. Key considerations when training supervised classifiers 39 2.2.3. Performance evaluation of supervised classifiers 41 2.3.Traditionalclassificationalgorithms 45 2.3.1. Support vector machines 45 2.3.2. Random forests 51 2.3.3. k-nearest neighbor 56 2.4. Classification strategies based on temporal feature representations 59 2.4.1. Phenology-based classification approaches 60 2.4.2 Dictionary-based classificationapproaches 61 2.4.3 Shapelet-based classificationapproaches 62 2.5.Deeplearningapproaches 63 2.5.1. Introduction to deep learning 64 2.5.2.Convolutionalneuralnetworks 68 2.5.3.Recurrentneuralnetworks 71 2.6.References 75 Chapter 3 Semantic Analysis of Satellite Image Time Series 85 Corneliu Octavian DUMITRU and Mihai DATCU 3.1. Introduction 85 3.1.1.TypicalSITSexamples 89 3.1.2. Irregular acquisitions 90 3.1.3.Thechapterstructure 96 3.2.WhyaresemanticsneededinSITS? 96 3.3.Similaritymetrics 97 3.4. Feature methods 98 3.5. Classification methods 98 3.5.1.Activelearning 99 3.5.2.Relevancefeedback 100 3.5.3. Compression-based pattern recognition 100 3.5.4.LatentDirichletallocation 101 3.6.Conclusion 102 vii 3.7.Acknowledgments 105 3.8.References 105 Chapter 4 Optical Satellite Image Time Series Analysis for Environment Applications: From Classical Methods to Deep Learning and Beyond 109 Matthieu MOLINIER, Jukka MIETTINEN,DinoIENCO,ShiQIU and Zhe ZHU 4.1. Introduction 109 4.2. Annual time series 111 4.2.1. Overview of annual time series methods 111 4.2.2 Examples of annual times series analysis applications for environmentalmonitoring 112 4.2.3.Towardsdensetimeseriesanalysis 116 4.3. Dense time series analysis using all available data 117 4.3.1. Making dense time series consistent 118 4.3.2. Change detection methods 121 4.3.3.Summaryandfuturedevelopments 125 4.4. Deep learning-based time series analysis approaches 126 4.4.1 Recurrent Neural Network (RNN) for Satellite Image TimeSeries 129 4.4.2 Convolutional Neural Networks (CNN) for Satellite Image TimeSeries 131 4.4.3. Hybrid models: Convolutional Recurrent Neural Network (ConvRNN) models for Satellite Image Time Series 134 4.4.4. Synthesis and future developments 136 4.5. Beyond satellite image time series and deep learning: convergence between time series and video approaches 136 4.5.1 Increased image acquisition frequency: from time series to spacebornetime-lapseandvideos 137 4.5.2. Deep learning and computer vision as technology enablers 138 4.5.3.Futuresteps 139 4.6.References 140 Chapter 5 A Review on Multi-temporal Earthquake Damage Assessment Using Satellite Images 155 Gülşen TAŞKIN, EsraERTEN and Enes Oğuzhan ALATAŞ 5.1. Introduction 155 5.1.1. Research methodology and statistics 159 5.2. Satellite-based earthquake damage assessment 165 5.3. Pre-processing of satellite images before damage assessment 167 5.4. Multi-source image analysis 168 5.5. Contextual feature mining for damage assessment 169 5.5.1.Texturalfeatures 170 5.5.2. Filter-based methods 173 5.6. Multi-temporal image analysis for damage assessment 175 5.6.1. Use of machine learning in damage assessment problem 176 5.6.2. Rapid earthquake damage assessment 180 5.7. Understanding damage following an earthquake using satellite-based SAR 181 5.7.1. SAR fundamental parameters and acquisition vector 185 5.7.2. Coherent methods for damage assessment 188 5.7.3. Incoherent methods for damage assessment 192 5.7.4. Post-earthquake-only SAR data-based damage assessment 195 5.7.5 Combination of coherent and incoherent methods for damage assessment 196 5.7.6.Summary 198 5.8. Use of auxiliary data sources 200 5.9.Damagegrades 200 5.10.Conclusionanddiscussion 203 5.11.References 205 Chapter 6 Multiclass Multilabel Change of State Transfer Learning from Image Time Series 223 Abdourrahmane M. ATTO,HélaHADHRI, FlavienVERNIER and Emmanuel TROUVÉ 6.1. Introduction 223 6.2. Coarse- to fine-grained change of state dataset 225 6.3. Deep transfer learning models for change of state classification 232 6.3.1.Deeplearningmodellibrary 232 6.3.2.GraphstructuresfortheCNNlibrary 234 6.3.3. Dimensionalities of the learnables for the CNN library 236 6.4.Changeofstateanalysis 237 6.4.1 Transfer learning adaptations for the change of state classificationissues 238 6.4.2.Experimentalresults 239 6.5.Conclusion 243 6.6.Acknowledgments 244 6.7.References 244 List of Authors 247 Index 249 Summary of Volume 1 253

Read more less

Book SynopsisFace analysis is essential for a large number of applications such as human-computer interaction or multimedia (e.g. content indexing and retrieval). Although many approaches are under investigation, performance under uncontrolled conditions is still not satisfactory. The variations that impact facial appearance (e.g. pose, expression, illumination, occlusion, motion blur) make it a difficult problem to solve.This book describes the progress towards this goal, from a core building block – landmark detection – to the higher level of micro and macro expression recognition. Specifically, the book addresses the modeling of temporal information to coincide with the dynamic nature of the face. It also includes a benchmark of recent solutions along with details about the acquisition of a dataset for such tasks.Table of ContentsPreface xiRomain BELMONTE and Benjamin ALLAERT Part 1. Facial Landmark Detection 1 Introduction to Part 1 3Romain BELMONTE, Pierre TIRILLY, IoanMarius BILASCO, Nacim IHADDADENE and Chaabane DJERABA Chapter 1. Facial Landmark Detection 13Romain BELMONTE, Pierre TIRILLY, IoanMarius BILASCO, Nacim IHADDADENE and Chaabane DJERABA 1.1. Facial landmark detection in still images 14 1.1.1.Generativeapproaches 14 1.1.2.Discriminative approaches 18 1.1.3.Deep learningapproaches 24 1.1.4.Handlingchallenges 34 1.1.5.Summary 40 1.2.Extendingfacial landmarkdetectionto videos 41 1.2.1.Trackingby detection 41 1.2.2.Box, landmarkand pose tracking 43 1.2.3.Adaptive approaches 45 1.2.4. Joint approaches 46 1.2.5. Temporal constrained approaches 47 1.2.6.Summary 49 1.3.Discussion 50 1.4.References 52 Chapter 2. Effectiveness of Facial Landmark Detection 67Romain BELMONTE, Pierre TIRILLY, IoanMarius BILASCO, Nacim IHADDADENE and Chaabane DJERABA 2.1.Overview 68 2.2.Datasets and evaluationmetrics 69 2.2.1. Image and videodatasets 69 2.2.2. Face preprocessing and data augmentation 73 2.2.3.Evaluationmetrics 75 2.2.4.Summary 77 2.3. Image andvideobenchmarks 77 2.3.1. Compiled results on 300W 77 2.3.2. Compiled results on 300VW 79 2.4.Cross-dataset benchmark 80 2.4.1.Evaluationprotocol 80 2.4.2.Comparisonof selected approaches 82 2.5.Discussion 86 2.6.References 88 Chapter 3. Facial Landmark Detection with Spatio-temporal Modeling 93Romain BELMONTE, Pierre TIRILLY, IoanMarius BILASCO, Nacim IHADDADENE and Chaabane DJERABA 3.1.Overview 94 3.2.Spatio-temporalmodelingreview 95 3.2.1.Hand-craftedapproaches 95 3.2.2.Deep learningapproaches 97 3.2.3.Summary 103 3.3.Architecturedesign 104 3.3.1. Coordinate regression networks 104 3.3.2.Heatmapregressionnetworks 106 3.4.Experiments 107 3.4.1.Datasets andevaluationprotocols 107 3.4.2. Implementationdetails 108 3.4.3.EvaluationonSNaP-2DFe 109 3.4.4. Evaluation on 300VW 111 3.4.5.Comparisonwith existingmodels 112 3.4.6. Qualitative results 112 3.4.7.Propertiesof the networks 114 3.5.Design investigations 114 3.5.1.Encoder-decoder 115 3.5.2. Complementarity between spatial and temporal information 117 3.5.3. Complementarity between local and global motion 119 3.6.Discussion 122 3.7.References 123 Conclusion to Part 1 133Romain BELMONTE, Pierre TIRILLY, IoanMarius BILASCO, Nacim IHADDADENE and Chaabane DJERABA Part 2. Facial Expression Analysis 147 Introduction to Part 2 149Benjamin ALLAERT, IoanMarius BILASCO and Chaabane DJERABA Chapter 4. Extraction of Facial Features 157Benjamin ALLAERT, IoanMarius BILASCO and Chaabane DJERABA 4.1. Introduction 157 4.2.Face detection 158 4.2.1.Point-of-interestdetectionalgorithms 160 4.2.2.Face alignment approaches 162 4.2.3.Synthesis 166 4.3.Face normalization 166 4.3.1.Dealingwith headpose variations 167 4.3.2.Dealingwith facial occlusions 170 4.3.3.Synthesis 172 4.4.Extractionof visual features 172 4.4.1.Facial appearancefeatures 172 4.4.2.Facial geometric features 174 4.4.3. Facial dynamics features 175 4.4.4.Facial segmentationmodels 177 4.4.5.Synthesis 179 4.5. Learning methods 179 4.5.1.Classification versus regression 180 4.5.2.Fusionmodel 182 4.5.3.Synthesis 184 4.6.Conclusion 185 4.7.References 186 Chapter 5. Facial Expression Modeling 191Benjamin ALLAERT, IoanMarius BILASCO and Chaabane DJERABA 5.1. Introduction 191 5.2.Modelingof the affective state 192 5.2.1.Categoricalmodeling 192 5.2.2.Dimensionalmodeling 194 5.2.3.Synthesis 196 5.3. The challenges of facial expression recognition 197 5.3.1. The variation of the intensity of the expressions 197 5.3.2.Variationof facialmovement 199 5.3.3.Synthesis 200 5.4.The learningdatabases 201 5.4.1. Improvementof learningdata 201 5.4.2. Comparison of learning databases 203 5.4.3.Synthesis 205 5.5. Invariance to facial expression intensities 206 5.5.1.Macro-expression 206 5.5.2.Micro-expression 208 5.5.3.Synthesis 209 5.6. Invarianceto facialmovements 211 5.6.1. Pose variations (PV) and large displacements (LD) 211 5.6.2.Synthesis 214 5.7.Conclusion 215 5.8.References 216 Chapter 6. Facial Motion Characteristics 223Benjamin ALLAERT, IoanMarius BILASCO and Chaabane DJERABA 6.1. Introduction 223 6.2.Characteristics of the facialmovement 225 6.2.1. Local constraint of magnitude and direction 226 6.2.2. Local constraint of the motion distribution 228 6.2.3.Motionpropagationconstraint 230 6.3.LMP 232 6.3.1. Local consistency of the movement 233 6.3.2.Consistencyof local distribution 236 6.3.3. Coherence in the propagationof themovement 238 6.4.Conclusion 241 6.5.References 242 Chapter 7. Micro- and Macro-Expression Analysis 243Benjamin ALLAERT, IoanMarius BILASCO and Chaabane DJERABA 7.1. Introduction 243 7.2. Definition of a facial segmentation model 244 7.3.Feature vector construction 247 7.3.1.Motionfeaturesvector 247 7.3.2.Geometric featuresvector 248 7.3.3.Features fusion 249 7.4. Recognition process 250 7.5. Evaluation on micro- and macro-expressions 251 7.5.1.Learningdatabases 252 7.5.2. Micro-expression recognition 253 7.5.3. Macro-expressions recognition 255 7.5.4. Synthesis of experiments on micro- and macro-expressions 258 7.6. Same expression with different intensities 260 7.6.1.Data preparation 260 7.6.2.Fractional time analysis 263 7.6.3.Analysis on a different time frame 264 7.6.4. Synthesis of experiments on activation segments 265 7.7.Conclusion 265 7.8.References 266 Chapter 8. Towards Adaptation to Head Pose Variations 271Benjamin ALLAERT, IoanMarius BILASCO and Chaabane DJERABA 8.1. Introduction 271 8.2.Learningdatabase challenges 273 8.3. Innovative acquisition system (SNaP-2DFe) 274 8.4. Evaluation of face normalization methods 276 8.4.1. Does the normalization preserve the facial geometry? 277 8.4.2. Does normalization preserve facial expressions? 280 8.5.Conclusion 283 8.6.References 284 Conclusion to Part 2 287Benjamin ALLAERT, IoanMarius BILASCO and Chaabane DJERABA List of Authors 293 Index 295

Read more less